XLM CV 1 of 2

7. Journal of Computational and Graphical Statistics

7.1     van Dyk, D.A. and Meng, X.-L. (1997). On the Orderings and Groupings of Conditional Maximizations within ECM-Type Algorithms. Vol. 6(2), p. 202-223. 

7.2     Meng, X.-L. (2000). Discussion of “Optimization Transfer Using Surrogate Objective Functions” by K. Lange, D. Hunter, and I. Yang. Vol. 9(1), p. 35-43.

7.3     van Dyk, D.A. and Meng, X.-L. (2001). The Art of Data Augmentation (with discussion and rejoinder). Vol. 10(1), p. 1-111.

7.4     Meng, X.-L. and Schilling, S. (2002). Warp Bridge Sampling. Vol. 11(3), p. 552-586.

7.5     Yu, Y. and Meng, X.-L. (2011). To Center or Not to Center, That Is Not the Question – An Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Efficiency (with Discussions). Vol. 20(3), p. 531-615. Main paper (p. 531-570), Supplement, Discussion (p. 571-602) and Rejoinder (p. 603-615).

7.6     Xu, X., Meng, X.-L., and Yu, Y. (2013). Thank God That Regressing Y on X is Not the Same as Regressing X on Y: Direct and Indirect Residual Augmentations. Vol. 22(3), p. 598-622.

7.7    Tak, H, Meng, X.-L., and Van Dyk, D. (2018) A Repelling-Attracting Metropolis Algorithm for Multimodality. Vol. 27(3), p. 479-490

8. Other Statistical Journals:

8.1     Gelman, A.E. and Meng, X.-L. (1991). A Note on Bivariate Distributions That Are Conditionally Normal. The American Statistician, Vol. 45(2), p. 125-126.

8.2     Meng, X.-L. (1993). On the Absolute Bias Ratio of Ratio Estimators. Statistics and Probability Letters, Vol. 18(5), p. 345-348.

8.3     Meng, X.-L. (1997). Discussion of “Statistical Inference and Monte Carlo Algorithms” by G. Casella. Test, Vol. 5(2), p. 310-318.

8.4     Meng, X.-L. (1997). The EM Algorithm and Medical Studies: A Historical Link. Statistical Methods in Medical Research, Vol. 6, p. 3-23. Response: Did Newton–Raphson really fail? (2014) Vol. 23(3), p. 312-314.

8.5     Barnard, J. and Meng, X.-L. (1999). Applications of Multiple Imputation in Medical Studies: From AIDS to NHANES. Statistical Methods in Medical Research, Vol. 8, p. 17-36.

8.6     Craiu, R. and Meng, X.-L. (2001). Chance and Fractals. Chance, Vol. 14, p. 47-52.

8.7     Meng, X.-L. and Romero, M. (2003). Discussion: Efficiency and Self-Efficiency with Multiple Imputation Inference. International Statistical Review, Vol. 71(3), p. 607-618.

8.8     Vaida, F. and Meng, X.-L. (2005). Two Slice-EM Algorithms for Fitting Generalized Linear Mixed Models with Binary Response. Statistical Modelling, Vol. 5, p. 229-242.

8.9     Meng, X.-L. and Vaida, F (2006). What's Missing for DIC With Missing Data? Comment on Article by Celeux et al. Bayesian Analysis, Vol. 1(4), p. 687-698.

8.10     Meng, X.-L. (2012). Enhanced Security Checks at Airports: Minimizing Time to Detection or Probability of Escape? Stat, Vol. 1(1), p. 42-52

8.11     Dasgupta, T. and Meng, X.-L. (2012). DoIt and Do It Well. Comment on an article by Roshan Joseph. Technometrics, Vol. 54(3), p. 227-231.

8.12     Blocker, A. and Meng, X.-L. (2013). The Potential and Perils of Preprocessing: Building New Foundations. Bernoulli, Vol. 19(4), p. 1176-1211

8.13     Liu, J. C., Meng, X.-L., Chen, C., Alegria, M. (2013). Statistics Can Lie But Can Also Correct for Lies: Reducing Response Bias in NLAAS via Bayesian Imputation. Statistics and Its Interface, Vol. 6, p. 387-398.

8.14     Liu, K. and Meng, X.-L. (2014). A Fruitful Resolution to Simpson's Paradox via Multi-Resolution Inference. The American Statistician, Vol. 68(1), p. 17-29.

8.15     Liu, K. and Meng, X.-L. (2016). There is Individualized Treatment. Why Not Individualized Inference? The Annual Review of Statistics and Its Applications, Vol. 3, p. 79-111.
8.16     Meng, X.-L. (2016). Discussion: Should a Working Model Actually Work? International Statistical Review, Vol. 84(3), p. 362-367.

8.17     Meng, X.-L.  (2022) Double Your Variance, Dirtify Your Bayes, Devour Your Pufferfish, and Draw Your Kidstogram (with Discussions)  New England Journal of Statistics in Data Science.  

8.18     Meng, X.-L. (2022) Miniaturizing data defect correlation: A versatile strategy for handling non-probability samples.  Survey Methodology  48(2).

8.19      Craiu, R,  Gong, R, and Meng, X.-L. (2023)  Six Statistical Senses. The Annual Review of Statistics and Its Applications. 10., 699-725.  

 

 

9. Subject-Specific Research Journals (Including Refereed Proceedings):

9.1     Meng, X.-L., Rosenthal, R. and Rubin, D.B. (1992). Comparing Correlated Correlation Coefficients. Psychological Bulletin, Vol. 111(1), p. 172-175.

9.2     Tu, X.M., Meng, X.-L. and Pagano, M. (1993). Survival Differences and Trends in Patients with AIDS in the United States. Journal of Acquired Immune Deficiency Syndromes, Vol. 6(10), p. 1150-1156.

9.3     Tu, X.M., Meng, X.-L. and Pagano, M. (1994). On the Use of Conditional Maximization in Chemometrics. The Journal of Chemometrics, Vol. 8, p. 365-370.

9.4     Meng, X.-L. and Rubin, D.B. (1994). On the Global and Componentwise Rates of Convergence of the EM Algorithm. Linear Algebra and Its Applications, Vol. 199, p. 413-425.

9.5     Weatherhead, E., Reinsel, G., Tiao, G.C., Meng, X.-L., Frederick, J., Choi, D., Cheang, W., Keller, T., DeLuisi, J., Wuebbles, D., Kerr, J., and Miller, J. (1998). Factors Affecting the Detection of Trends: Statistical Considerations and Applications to Environmental Data. Journal of Geophysical Research, Vol. 103, p. 17,149-17,161.

9.6     Alegria, M., Takeuchi, D., Canino, G., Duan, N., Shrout, P., Meng, X.-L., Vega, W., Zane, N., Vila, D., Woo, M., Vera, M., Guarnaccia, P., Aguilar-Gaxiola, S., Sue, S., Escobar, J., Lin, K-M, Gong, F. (2004). Considering Context, Place, and Culture: The National Latino and Asian American Study. International Journal of Methods in Psychiatric Research, Vol. 13(4), p. 208-220.

9.7     Lee, T.C.M. and Meng, X.-L. (2005). A Self-Consistent Wavelet Method for Denoising Images with Missing Pixels. Proceedings of the 30th IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.2, p. 41-44.

9.8     Hirakawa, K and Meng, X.-L. (2006). An Empirical Bayes EM-Wavelet Unification for Simultaneous Denoising, Interpolation, and/or Demosaicing. Proceedings of the 2006 IEEE International Conference on Image Processing, p. 1453-1456.

9.9     Miller, A.J., Cai, A., Tiao, G., Wuebbles, D.J., Flynn, L.E., Yang, S-K., Weatherhead, E.C., Fioletov, V., Petropavlovskikh, I., Meng, X.-L., Guillas, S., Nagatani, R.M. and Reinsel, G.C. (2006). Examination of Ozonesonde Data for Trends and Trend Changes Incorporating Solar and Arctic Oscillation Signals. Journal of Geophysical Research, Vol. 111, D13305, p. 1-10.

9.10     Hirakawa, K, Meng, X.-L., Wolfe, P.J. (2007). A Framework for Wavelet-Based Analysis and Processing of Color Filter Array Images with Applications to Denoising and Demosaicing. Proceedings, IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 1, p. 597-600.

9.11     Meng, X.-L. (2007). A Helicopter View of The Self-Consistency Framework for Wavelets and Other Signal Extraction Methods In the Presence of Missing and Irregularly Spaced Data. Wavelets XII, edited by Dimitri Van De Ville, Vivek K. Goyal, Manos Papadakis, Proceedings of SPIE Vol. 6701 (SPIE, Bellingham, WA, 2007) 670124.

9.12     Alegria, M., Woo, M., Cao, Z., Torres, M., Meng, X.-L. and Striegel-Moore, R. (2007). Prevalence and Correlates of Eating Disorders in Latinos in the United States. International Journal of Eating Disorders, 40, S15-S21.

9.13     Duan, N., Meng, X.-L., Lin, J., Chen, C. and Alegria, M. (2008). Disparities in Defining Disparities: Statistical Conceptual Frameworks. Statistics in Medicine, Vol. 27(20), p. 3941-3956.

9.14     Alegria, M., Canino, G., Shrout, P., Woo, M., Duan, N., Vila, D., Torres, M., Chen, C. and Meng, X.-L. (2008). Prevalence of Mental Illness in Immigrant and Non-Immigrant U.S. Latino Groups. American Journal of Psychiatry, Vol. 165, p. 359-369.

9.15     Alegría, M, Chatterji, P, Wells, K, M.D., Cao, Z, Chen, C-N.,Takeuchi, D. Jackson, J. Meng, X.-L. (2008). Disparity in Depression Treatment Among Racial and Ethnic Minority Populations in the United States. Psychiatric Services, Vol. 59(11), p. 1264-1272.

9.16     Lee, H, Kashyap, V. L., van Dyk, D.A., Connors, A., Drake, J.J., Izem, R., Meng, X.-L., Min, S., Park, T., Ratzlaff, P., Siemiginowska, A., and Zezas, A. (2011). Accounting for Calibration Uncertainties in X-ray Analysis: Effective Areas in Spectral Fitting. The Astrophysics Journal, Vol. 731, p. 126 -144.

9.17     Meng, X.-L. (2012). You Want Me to Analyze Data I Don’t Have? Are You Insane? Shanghai Archives of Psychiatry, Vol. 24(5), p. 297-301.

9.18     Stein, N., Kashyap, V., Meng, X.-L., and van Dyk, D.A. (2012). H-Means Image Segmentation to Identify Solar Thermal Features. Proceedings of the International Conference on Image Processing, ICIP 2012 (Editor: E. Saber), p. 1597-1600.

9.19     Meng, X.-L. and Xie, X. (2014). I Got More Data, My Model Is More Refined, But My Estimator Is Getting Worse! Am I Just Dumb? Econometric Reviews, 33: 218-250.

9.20     Xu, J., van Dyk, D. A., Kashyap, V. L., Siemiginowska, A., Connors, A., Drake, J. J., Meng, X. L., Ratzlaff, P., and Yu, Y. (2014). A Fully Bayesian Method for Jointly Fitting Instrumental Calibration and X-ray Spectral Models. The Astrophysical Journal, 794:97 (21pages)

9.21      Gong, R and Meng, X.-L. (2020)  Congenial Differential Privacy under Mandated Disclosure. FODS '20: Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference October 2020, 59–70 (https://doi.org/10.1145/3412815.3416892)

9.22      Bradley, Valerie C and Kuriwaki, Shiro and Isakov, Michael and Sejdinovic, Dino and Meng, X.-L.  and Flaxman, Seth (2021) Unrepresentative Big Surveys Significantly Overestimated US Vaccine Uptake.  Nature, 600, 695–700

Research Articles in Refereed Volumes and Encyclopedia:

1.     Meng, X.-L. and Rubin, D.B. (1992). Recent Extensions to the EM Algorithm (with Discussion). Bayesian Statistics 4, p. 307-320.

2.     Meng, X.-L. and Rubin, D.B. (1996). Efficient Methods for Estimation and Testing with Seemingly Unrelated Regressions in the Presence of Latent Variables and Missing Observations. In Bayesian Analysis of Statistics and Econometrics: Essays in Honor of Arnold Zellner (Eds: D.A. Berry, K. M. Chalonera and J.K. Geweke.), p. 215-227. New York: Wiley.

3.     Gelman, A.E. and Meng, X.-L. (1996). Model Checking and Model Improvement. In Practical Markov Chain Monte Carlo (Eds: W. Gilks, S. Richardson, and D. Spiegelhalter), p. 189-201. London, U.K.: Chapman and Hall.

4.     van Dyk, D.A. and Meng, X.-L. (2000). The EM Algorithm. In The Encyclopedia of Mathematics, Supplement II (Editor-in-Chief, M. Hazewinkel; Coordinating Editor, R. Hoksbergen), p. 192-195. Dordrecht: Kluwer Academic Publishers.

5.     Meng, X.-L. (2000). Towards a More General Propp-Wilson Algorithm: Multistage Backward Coupling. In Fields Institute Communications Series 26: Monte Carlo Methods (American Mathematical Society), p. 85-93.

6.     Meng, X.-L. (2001). A Congenial Overview and Investigation of Multiple Imputation Inference Under Uncongeniality. A chapter for Survey Nonresponse (Eds: R. Groves, D. Dillman, J. Eltinge and R. Little), p. 343-356. New York: Wiley.

7.     Craiu, R. and Meng, X.-L. (2001). Antithetic Coupling for Perfect Sampling. In Bayesian Methods, with Applications to Science, Policy and Official Statistics (proceedings of the ISBA 2000 conference, Hersonnissos, Crete) (Ed: E. I. George), p. 99-108. Luxembourg: Office for Official Publications of the European Communities.

8.     Murdoch, D. and Meng, X.-L. (2001). Towards Perfect Sampling for Bayesian Mixture Priors. In Bayesian Methods, with Applications to Science, Policy and Official Statistics (proceedings of the ISBA 2000 conference, Hersonnissos, Crete) (Ed: E. I. George), p. 381-390. Luxembourg: Office for Official Publications of the European Communities.

9.     Kong, A., McCullagh, P., Meng, X.-L. and Nicolae, D. (2006). Further Explorations of Likelihood Theory for Monte Carlo Integration. Advances in Statistical Modeling and Inference: Essays in Honor of Kjell A. Doksum (Ed: V. Nair) New Jersey: World Scientific Press, p. 563-592.

10.     Meng, X.-L. (2006). The EM Algorithm. In Encyclopedia of Statistical Sciences, Update Vol. 1 (Editor-in-chief, S. Kotz; Executive Editor, C.B. Read), p. 218-227. New York, Wiley.

11.     Servidea, J. and Meng, X.-L. (2006). Statistical Physics and Statistical Computing: A Critical Link. Frontiers in Statistics: Dedicated to Peter John Bickel in Honor of his 65th Birthday (Eds: J. Fan and H.L. Koul). London: Imperial College Press, p. 327-244.

12.     Meng, X.-L. (2008). Inference, Statistical. International Encyclopedia of the Social Sciences. Ed. William A. Darity, Jr.. Vol. 4. 2nd ed. Detroit: Macmillan Reference USA, p. 15-18.

13.     Meng, X.-L. (2009). Automated Bias-variance Trade-off: Intuitive Inadmissibility or Inadmissible Intuition? In Frontiers of Statistical Decision Making and Bayesian Analysis: in honor of James O Berger’s 60th Birthday (Eds: M-H Chen, D-K. Dey, P. Mueller, D. Sun, and K. Ye). Springer, New York, p. 95-112.

14.     Meng, X.-L. (2011). Multi-Party Inference and Uncongeniality. International Encyclopedia of Statistical Science (Ed: Miodrag Lovric). Springer, New York, 884-888.

15.     Meng, X.-L. (2011). What's the H in H-likelihood: A Holy Grail or An Achilles' Heel? (with discussions). In Bayesian Statistics 9 (Eds. J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West). Oxford University Press, Chapter 16.

16.     Craiu, R and Meng, X.-L. (2011). Perfection within Reach: Exact MCMC Sampling. In Handbook on Markov Chain Monte Carlo: Methods and Applications (Eds: S. Brooks, J. Galin, A. Gelman, and X.-L., Meng). Chapman & Hall/CRC Press, 199-226.  (Updated Chapter: Perfecting MCMC Sampling: Recipes and Reservations)

17.     Meng, X.-L. (2014). A Trio of Inference Problems that Could Win You a Nobel Prize in Statistics (If You Help Fund It). In Past, Present, and Future of Statistical Science (Eds: X. Lin, et. al), CRC Press, p. 537-562.

18         Gong,  R. and Meng, X.-L. (2020) Congenial Differential Privacy under Mandated Disclosure. In Proceedings of the 2020 ACM-IMS Foundations of Data Science Conference (FODS '20), October 19--20, 2020, Virtual Event, USA. https://dl.acm.org/doi/10.1145/3412815.341689

19         Baines, P.D,  Meng, X.-L.,  Zezas, A.  Kashyap, V.  (2020)  Colorful Stars and Black Boxes: Bayesian Analysis of Stellar Populations.  In Statistics in the Public Interest – In Memory of Stephen E. Fienberg (Eds: A. Carriquiry, B. Eddy, J. Tanur, and M. Smykla), Springer.

20         Marshall, H. L., Chen, Y., Drake, J. J., Guainazzi, M., Kashyap, V. L., Meng, X.-L., Plucinsky, P. P.,Ratzlaff, P., van Dyk, D. A., and Wang, X. (2021), Concordance: In-flight Calibration of X-Ray Telescopes without Absolute References. The Astronomical Journal, 162, 254.

21        Biswas, N, Mackey,  L, and Meng, X.-L.  (2022) Scalable Spike-and-Slab.  In Proceedings of the 39th International Conference on Machine Learning, Baltimore, Maryland, USA, PMLR 162; 20 pages 22        

22           Meng, X.-L. (2024)  A BFFer's Exploration with Nuisance Constructs:  Bayesian p-value, H-likelihood, and Cauchyanity.   In Handbook on Bayesian, Fiducial, and Frequentist Inference.  (Eds. Berger, Meng,  Reid, and Xie) Chapman & Hall/CRC Press.

 

 

Research Articles in Un-refereed Conference Proceedings: 

1.     Meng, X.-L. and Rubin, D.B. (1989). Obtaining Asymptotic Variance-Covariance Matrices by the EM Algorithm. The Proceedings of the Statistical Computing Section of the American Statistical Association, 140-144.

2.     Meng, X.-L. and Rubin, D.B. (1990). Likelihood Ratio Tests with Multiply-Imputed Data. The Proceedings of the Statistical Computing Section of the American Statistical Association, 78-82.

3.     Meng, X.-L. and Rubin, D.B. (1991). IPF for Contingency Tables with Missing Data via the ECM Algorithm. The Proceedings of the Statistical Computing Section of the American Statistical Association, 244-247.

4.     Meng, X.-L. and Pedlow, S. (1992). EM: A Bibliographic Review with Missing Articles. The Proceedings of the Statistical Computing Section of the American Statistical Association, 24-27.

5.     Barnard, J. and Meng, X.-L. (1994). Exploring Cross-Match Estimators with Multiply Imputed Datasets. The Proceedings of the Survey Research Methods Section of the American Statistical Association, 894-899.

6.     van Dyk, D.A. and Meng, X.-L. (1994). Permuting CM Steps within the ECM Algorithm. The Proceedings of the Statistical Computing Section of the American Statistical Association, 130-135.

7.     Pedlow, S. and Meng, X.-L. (1994). Exploring Hypothesis-Testing Procedures with Multiply-Imputed Data Sets Under Unequal Fractions of Missing Information. The Proceedings of the Survey Research Methods Section of the American Statistical Association, 595-599.

8.     Meng, X.-L. and Zaslavsky, A. (1994). Single Observation Unbiased Priors. The Proceedings of the Section on Bayesian Statistical Science of the American Statistical Association, 46-51.

9.     Meng, X.-L. and van Dyk, D.A. (1995). Augmenting Data Wisely to Speed up the EM Algorithm. The Proceedings of the Statistical Computing Section of the American Statistical Association, 160-165.

10.     Meng, X.-L. and van Dyk, D.A. (1996). Minimum Information Ratio and Relative Augmentation Function. The Proceedings of the Statistical Computing Section of the American Statistical Association, 73-78.

11.     van Dyk, D.A. and Meng, X.-L. (2000). Algorithms Based on Data Augmentation: A Graphical Representation and Comparison. In Models, Predictions, and Computing: Proc. 31st Symp. Interface (Eds: M. Pourahmadi and K. Berk), 230-239.

12.     Meng, X.-L., Alegria, M., Chen, C.N. and Liu, J. (2005). A Nonlinear Hierarchical Model for Estimating Prevalence Rates with Small Sample. Proceedings of the Bayesian Statistics Section of the American Statistical Association, 110-120.

13.     Liu, J., Meng, X.-L., Chen, C.N., Alegria, M. (2006). Multiple Imputation for Response Biases in NLAAS Due to Survey Instruments. Proceedings of the Survey Research Methods Section of the American Statistical Association, 3360-3366. (CD-ROM)

14.     Chen, C.N., Duan, N, Meng, X.-L., Alegria, M. (2006). Power-Shrinkage and Trimming: Two ways to Mitigate Excessive Weights. Proceedings of the Survey Research Methods Section of the American Statistical Association, 2839-2846. (CD-ROM)

15.     Baines, P.D. and Meng, X.-L. (2008). Probability Matching Priors in LHC Physics. CERN Yellow Report: Proceedings of Phystat-LHC Workshop in Statistical Issues for LHC Physics, 135-138.

Short Communications (Short Discussions, Comments, Letters, Responses, etc.)

• 1.     Gelman, A.E. and Meng, X.-L. (1995). Discussion of “Fractional Bayes Factors for Model Comparison,” by A. O’Hagan. Journal of the Royal Statistical Society Series B, Vol. 57, p. 99.
• 2.     Gelman, A.E. and Meng, X.-L. (1995). Discussion of “Assessment and Propagation of Model Uncertainty,” by D. Draper. Journal of the Royal Statistical Society Series B, Vol. 57(1), p. 45.
• 3.     Meng, X.-L. (1995). Comment on “A Characterization of the Poisson Distribution and the Probability of Winning a Game,” by J. B. Keller. The American Statistician, Vol. 49(4), p. 401.
• 4.     Meng, X.-L. (1995). Letter to The Editor: “Double Effort, Not Double Blind!” Amstat News, April, p. 8.
• 5.     Meng, X.-L. (1995). Letter to The Editor: “A Question of Practicality.” RSS News, April, p. 4.
• 6.     Stein, M.L. and Meng, X.-L. (1995). Report on 1995 IMS/ASA Invited Panel on “Speeding the Referee Process.” IMS Bulletin, 24, 607-608.
• 7.     Meng, X.-L. (1998). Letter to The Editor: “Team Work.” RSS News, June, p. 6.
• 8.     Meng, X.-L. and Gelman, A.E. (1999). Discussion of “Quantifying Surprise in the Data and Model Verification” by M.J. Bayarri and J. O. Berger. Bayesian Statistics 6
• 9.     Meng, X.-L. (1999). Comment on “Correlation, Regression Lines, and Moments of Inertia,” by R. B. Nelsen. The American Statistician, Vol. 53(2), p. 173.
• 10.     Meng, X.-L. (1999). Invited Discussion of Matthew Stephens’s and Simon Tavaré’s papers on Statistical and Computational Approaches to Genetic Evolution. In Bulletin of the International Statistical Institute; 52nd Session, Helsinki, Vol. 3, 111-112.
• 11.     Meng, X.-L. (2000). Discussion of “Inference in Molecular Population Genetics” by M. Stephens and P. Donnelly. Journal of the Royal Statistical Society B, Vol. 62, p. 648-649.
• 12.     Meng, X.-L. (2000). Letter to The Editor: “Sod’s Law in Action.” RSS News, December, p. 10.
• 13.     Meng, X.-L. (2001). MCMC Versus BS: A Statistical Controversy? International Chinese Statistical Association (ICSA) Bulletin, July 2001, p. 38-42.
• 14.     Meng, X.-L. (2002). Discussion of “Bayesian Measures of Model Complexity and Fit” by D.J. Spiegelhalter, N.G. Best, B.P. Carlin, and A. van der Linde. Journal of the Royal Statistical Society B, Vol. 64(4), p. 633.
• 15.     Meng, X.-L. (2002). Discussion of “Statistical Modelling and Analysis of Genetic Data”, a collection of papers in Journal of the Royal Statistical Society B, Vol. 64(4), p. 756.
• 16.     Meng, X.-L. (2003). Discussion of “Efficient Construction of Reversible Jump Markov Chain Monte Carlo Proposal Distributions” by S.P. Brooks, P. Giudici, and G.O. Roberts. Journal of the Royal Statistical Society B, Vol. 65(1), p. 42.
• 17.     Meng, X.-L. (2008). Discussion of “Sampling Bias and Logistic Models” by P. McCullagh. Journal of the Royal Statistical Society B, Vol. 70(4), p. 672.
• 18.     Meng, X.-L. (2011). Discussion of “Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods” by M. Girolami and B. Calderhead. Journal of the Royal Statistical Society B, Vol. 73(2), p. 190.
• 19.     Meng, X.-L. (2011). Discussion of “Towards more accessible conceptions of statistical inference” by C. J. Wild, M. Pfannkuch, M. Regan, and N. J. Horton. Journal of the Royal Statistical Society A, Vol. 174(2), p. 285.
• 20.     Meng, X.-L. (2013). Book Review of “Monte Carlo Statistical Methods” by C. Robert and G. Casella in Special Issue on George Casella’s Books. Chance, Vol. 26(1), p. 31-32.
• 21.     Meng, X.-L. (2016.) Discussion of “Perils and potentials of self-selected entry to epidemiological studies and surveys” by N. Keiding and T.A. Louis. Journal of the Royal Statistical Society A, Vol. 179(2), p. 319-376.
• 22.     Meng, X.-L. (2017). Discussion of “Should we sample a time series more frequently?: Decision support via multirate spectrum estimation” by Nason, G, Powell, BJ, Elliott, D & Smith, P. Journal of the Royal Statistical Society A, Vol. 180(2), p. 389.
• 23      Craiu, R. and Meng, X.-L. (2020) Discussion of “Unbiased Markov chain Monte Carlo methods with couplings” by P. Jacob, J.  O’Leary, and Y. Atchade.  Journal of the Royal Statistical Society B, 184.   578-581.
• 24.      Meng, X.-L. (2021)  Discussion of “Testing by betting: A strategy for statistical and scientific communication” by Glenn Shafer.  Journal of the Royal Statistical Society B, 184.  442-443. 
• 25.        Meng, X.-L. (2021) Discussion of “The Foundations of Statistical Science: A History of Textbook Presentations” by Alan Agresti, Brazilian Journal of Probability and Statistics, 35(4), 657-698

 

Other Manuscripts:

Technical and Internal Research Reports

• Significance Levels From the Repeated Significance Levels in Multiple Imputation. Ph.D. Qualifying Paper, Department of Statistics, Harvard University (1988).
• Coherent Multiple-Imputation Inference Under Incoherent Models.  Technical Report 359, Department of Statistics, The University of Chicago.
• Double Effort, Not Double Blind!  Technical Report 382, Department of Statistics, The University of Chicago.
• On Analytic Evaluation of Moments of Ratios and Logarithms. Technical Report 419, Department of Statistics, The University of Chicago.
• Efficient Data Augmentation: From the EM Algorithm to The Gibbs Sampler. Technical Report 458, Department of Statistics, The University of Chicago. (With D. van Dyk)
• Analysis of Solar Radiation Measurements at High Southern Latitudes.  Report to Ultraviolet Monitoring and Assessment Program. (With G. Tiao, J. Frederick, D. Choi, and Z. Qu)

Forthcoming (Hopefully)

• Likelihood-free EM: Self Consistency as a Dual Principle for Incomplete or Irregular-Pattern Data (With T. Lee and Z. Li).
• Variations on a thEMe:   Insights, Intrigues, and the Interwoven EM (With P. Baines and X. Xie).
• Designing Test Information and Test Information in Design. (With D. E. Jones.) 
• Probabilistic Underpinning of Imprecise Probability and Statistical Learning with Low-Resolution Information. (With R. Gong.)
• Data Quality and Sampling Schemes for Estimating Population Extremes (with J. Zhou.)
• Contemplating Breiman's Two Cultures: Holistic Stochastic Modeling for Simulation or Modulated Deterministic Molding for Emulation?
• Statistical Paradises and Paradoxes in Big Data (II): Multi-resolution Inference, Simpson’s Paradox, and Individualized Treatments.
• Understanding the Power of Solar Flare --- Fitting a Partially parametric Density via a Largely Overlooked Method: MPS (With X. Wang)
• From Predicting Presidential Elections to Assessing COVID Vaccination:  How Small Are Our Big Data?
• Inferential Monte Carlo Integration: A Likelihood Paradox or Bayesian Paradox? (With M. Uehara)
•  Channeling Multimodality through a Unimodalizing Transport:  Warp-U MCMC and Stochastic Bridge Sampling. (With F. Ding, D. E. Jones, and S. He.) submitted to JRSSB
• BELIEF in Dependence: Leveraging Atomic Linearity in Data Bits for Rethinking Generalized Linear Models.
• (With B. Brown and K. Zhang) Submitted to The Annals of Statistics.
• Can swapping be differentially private? A refreshment stirred, not shaken. (With J. Bailie and R. Gong)
• Congenial Differential Privacy under Mandated Disclosure: Painting a Deeper Picture. (With J. Bailie and R. Gong)
• Half-Cauchy Combination Test and Confidence Regions (with Tyler Liu and Natesh Pillar)
• Navigating Data Science Challenges Via Multiple Imputation in Exploring Complex Societal Issues: Findings From a Study On Assessing Living Conditions In Africa (with M. Kakooei, J. Bailie, and A. Daoud)
• Comparison of Goodness–of–fit Assessment Methods with C statistics in Astronomy (with X. Li, Y, Chen, and V. Kashyap)
• On Simultaneous Full-information Learning and Actual Error Assessment:  A Heisenberg-esque Uncertainty Principle for Statistical Learning?

 

Ph.D. Advisees and Co-Advisees at Harvard:

 

James Bailie, title TBD.

Niloy Biswas, Contributions to Scalable Bayesian Computation. (2022)

Kathryn McKeough,  A Tale of Two Multi-Phase Inference Applications (2020).   (Current Position:  Senior Analyst, Boston Red Sox).

Luis Fernando Campos Manzo, Fortunes and Misadventures with Parametric Models: They Can Confound, Be Burdensome

and Unstable, Yet Insightful, Powerful and Flexible (2019).  (Current Position: Senior Data Scientist at Etsy, Inc)

Kin Wai (Keith) Chan, Inference for Incomplete Data and Dependent Data (2018). (Current Position: Assistant Professor of

Statistics, Chinese University of Hong Kong)

Ruobin Gong, Low-resolution Statistical Modeling with Belief Functions (2018).  (Current Position: Assistant Professor of

Statistics, Rutgers University)

Xufei Wang, G-squared Statistic for Detecting Dependence, Additive Modeling, and Calibration Concordance for Astrophysical Data. (First Position: Quantitative Researcher, Two Sigma Investments, LLC)

Hyungsuk Tak, Topics in Bayesian Hierarchical Modeling and Monte Carlo Computation (2016). (First Position: Postdoc at

Statistical and Applied Mathematical Sciences Institute).

David Jones, Information: Measuring the Missing, Using the Observed, and Approximating the Complete (2016). (First

Position: Postdoc at Statistical and Applied Mathematical Sciences Institute).

Lazhi Wang, Methods in Monte Carlo Computation, Astrophysical Data Analysis and Hypothesis Testing with Multiply-

Imputed Data (2015) (First Position:  Quantitative Researcher, Two Sigma Investments, LLC).

Alex Blocker, Distributed and Multiphase Inference in Theory and Practice: Principles, Modeling, and Computation for High-

Throughput Science (2013). (First Position:  Statistician on Google Ads Quality Team).

Nathan Stein, Advances in empirical Bayes modeling and Bayesian computation (2013). (First Position: Shepp Research

Fellow, Lecturer in Statistics, Wharton School, University of Pennsylvania).

Xiaojin Xu, Methods in Hypothesis Testing, Markov Chain Monte Carlo and Neuroimaging Data Analysis (First

Position: Algorithmic Developer, Hudson River Trading, LLC).

Xianchao Xie, Two Tales of Frequentist Properties of Bayesianly Motivated Methods: Multiple Imputation and Shrinkage Estimation (2012). (First Position:  Quantitative Researcher, Two Sigma Investments, LLC).

Li Zhan, Statistical Missing Data and Computation Problems: Theories and Applications in Astrophysics, Finance and Economics (2010) (First Position:  Associate, Deutsche Bank).

Paul Baines, Statistics, Science and Statistical Science: Modeling, Inference and Computation with Applications to the Physical Sciences (2010).  (First Position: Assistant Professor of Statistics, UC Davis).

Yves Chretien, Three Applications of Statistics to Medical Research (2010).  (First Position: Lecturer in Statistics, Harvard).

Jingchen Liu, Effective Modeling and Scientific Computation with Applications to Health Study, Astronomy, and Queueing Network (2008).  (Current Position: Associate Professor of Statistics, Columbia University).

Taeyoung Park, Improving the Incomparable Gibbs Sampler Through Partial Marginalization and Partial Blocking (2006). (Current Position: Assistant Professor of Statistics, Yonsei University in Korea).

Yaming Yu, Three Contributions to Statistical Computing (2005).  (Current Position:  Associate Professor of Statistics, University of California, Irvine).

 

Former Ph.D. Advisees From Department of Statistics, The University of Chicago:

David van Dyk, Construction, Implementation, and Theory of Algorithms Based on Data Augmentation and Model Reduction (1995).  (Current Position: Chair Professor of Statistics, Department of Mathematics, Imperial College of London).

John Barnard, Cross-match Procedures for Multiple-Imputation Inference: Bayesian Theory and Frequentist Evaluations (1995).  (Current Position: Director, Collaborative Biostatistics Center, Cleveland Clinic Foundation).

Florin Vaida, At the Confluence of the EM Algorithm and Markov Chain Monte Carlo: Theory and Applications (1998).  (Current Position: Professor, Division of Biostatistics & Bioinformatics, UCSD).

Radu Craiu, Multivalent Framework for Approximate and Exact Sampling and Resampling (2001).  (Current Position:  Professor, Department of Statistics, University of Toronto).

James Servidea, Bridge Sampling with Dependent Random Draws: Techniques and Strategy (2002).  (Current Position:  Statistician, National Security Agency, U.S.A.).

Martin Romero, On Two Topics With No Bridge: Bridge Sampling with Dependent Draws and Bias of the Multiple Imputation Variance Estimator (2003).  (First Position: Post-doc, Instituto Mexicano del Petróleo).

Zhiqiang Tan, A Likelihood Approach For Monte Carlo Integration (2003).  (Current Position:  Professor, Department of Statistics, Regents University).

Peter Bouman, Statistical and Computational Inference for Complex Multicenter Studies (2004).  (First Position: Assistant Professor, Department of Marketing, Northwestern University).

 

Other Advisees and Co-advisees:

Jason Zhou, B. A. in Statistics (2023), Harvard University.  Senior Thesis: Data Quality Always Matters: An Analysis of Data

Quality for Finite Population Z-Estimators.

Robert McKenzie, B. A. in Statistics (2023), Harvard University.  Data Minding (and Mining) for Modern Science.

Keli Liu, B.A. in Statistics (2013), Harvard University. Senior Thesis:  Objective Bayes: Neither Objective Nor Bayes.

Alec Yeh, B.A. in Statistics and African and African American Studies (2014), Harvard University. Senior Thesis: Evaluation

of a Service Provider Short Course for Prevention of Fetal Alcohol Syndrome.

Stephen Schilling, M.A. in Statistics and Ph.D. in Psychology (1994), The University of Chicago.   (First Position: Assistant Professor, Department of Psychology and Human Development, Vanderbilt University).

Sheri Yung Hsueh, M.A. in Statistics (1993) and Ph.D. in Sociology (1996), The University of Chicago.  (First Position: Assistant Vice President, First Card, Chicago).

Kimberly Maier, Ph.D. in Education (2000), The University of Chicago.  (First Position: Post-doctoral Research Fellow, NORC, The University of Chicago).

Steven Pedlow, M.A. in Statistics (2001), The University of Chicago.  (First Position: Survey Statistician, NORC).

Henrik Støvring, Ph.D. in Statistics and Demography (2002), Odense Universitet, Denmark.

Spyros Konstantopoulos, M.A. in Statistics and Ph.D. in Education (2003), The University of Chicago.  (First Position: Assistant Professor, School of Education, Northwestern University).

 

Invited Pedagogical and Professional Development Presentations

6/7/04                “W.R.I.T.E.”  Workshop: Better Writing Skills for Success, ICSA Applied Symposium, San Diego, CA.

3/17/07              “Teaching and Career Development at Harvard: Task Force Recommendations” Panel Presentation to the Committee on University Resources (COUR) – Annual Symposium, Harvard University.

9/11/07              “What Faculty Course Heads and TFs Should Expect from Each Other?” Panel Presentation, 13^th Harvard Fall Teaching Conference.

4/29/08              “Statistics 105 - Real-Life Statistics: Your Chance for Happiness (or Misery)” Webinar for Section on Teaching Statistics in Health Sciences of ASA.

6/4/08                “Recent Pedagogical Innovations at Harvard Statistics” Presentation to GSAS (Graduate School of Arts and Sciences) Alumni Association Council, Harvard University. 

9/5/08                “Working with Teaching Fellows” Panel Presentation at Junior Faculty Institute, FAS, Harvard University.

10/3/08              “Developing a Professional Curriculum for Graduate Students” Presentation at the 2008-2009 DGS (Director of Graduate Studies) Retreat, Harvard University.

11/18/08           “Statistics 105 - Real-Life Statistics: Your Chance for Happiness (or Misery)"   Webinar for CAUSE (Consortium for the Advancement of Undergraduate Statistics Education).

2/27/09              “Statistics 105 - Real-Life Statistics: Your Chance for Happiness (or Misery)" Two-hour seminar at   Department of Statistics, University of Kentucky.

4/15/09              “Reflections on Rejections” Panel Presentation, Sponsored by the Bureau of Study Counsel and the Office of Career Services, Harvard  (Media Coverage:  Boston Global, April 21 and Harvard  Gazette, April 22; see http://www.boston.com/news/local/massachusetts/articles/2009/04/21/accepting_rejection/; http://news.harvard.edu/gazette/story/2009/04/the-upside-of-rejection/)

5/7/09                “Chair’s Closing (A)musing: Think Outside the Box but Stay Inside the Circle” Presentation at Professional Development Retreat, organized by Department of Statistics, Harvard University.

9/15/09              “Think Outside the Box But Stay Inside the Circle” Presentation to Fudan Alumni Association at Boston.

11/4/09              “Originality & Plagiarism in the Age of Cut-and-Paste” Panel Presentation, sponsored by the Bureau of Study Counsel, the Derek Bok Center for Teaching and Learning, the Harvard College Writing Program, the Program in General Education, and the Office of Undergraduate Education.

11/7/09              “Harvard Experiments” Two-and-half-hour presentation at New England Isolated Statisticians Meeting 14, Pine Manor College.

1/25/10              “Reflections on Rejections” Panel Presentation for Graduate School of Arts and Sciences (GSAS), sponsored by the Bureau of Study Counsel and the Office of Career Services, Harvard University.

3/26/10              “Harvard Experiments” Two-hour presentation and discussion at Department of Statistics, Orebro University, Sweden.

4/17/10              “Research Cultivation and Culmination: How to Get Your Paper Published (Eventually)” Short Course co-taught with J. Blitzstein during 2010 New England Statistics Symposium held at Harvard University.  

4/25/10              “Conversation: Insights on Effective Teaching” Panel Presentation:  Seminar on Teaching for Effective Learning for a delegation from Universidad Adolfo Ibáñez (UAI) in Chile.

7/12/10              “Real-Life Module Statistics: A Happy Harvard Experiment” Presentation at 8^th International Conference on Teaching Statistics, Ljubljana, Slovenia.

7/21/10              “Vital Statistics for Medical and Life Sciences: A Harvard Experiment” Shanghai International Workshop in Biostatistics, Shanghai, China.

8/5/10                “The Making of Sexy Statistics (and Statisticians): Some Harvard Experiments.”  Presentation for Panel on Statistics Training in a Data-Centric World, organized by The American Statistician, for 2010 JSM.  Vancouver, Canada.

10/4/10              “The Making of Sexy Statistics (and Statisticians): Some Harvard Experiments.”  Department of Statistics, University of British Columbia, Vancouver, Canada.

10/22/10           “The Making of Sexy Statistics (and Statisticians): Some Harvard Experiments.”  Presentation for Panel on Training the Next Generation of Quantitative Scientists, Symposium in Honor of Steve Lagakos, Department of Biostatistics, Harvard University.

4/2/11                “Why did I choose Harvard?”  Presentation for One Harvard Faculty-Alumni/ae Panel, sponsored by the Office of the President and Provost, Harvard University.

4/6/11                “Reflections on Rejections; An Exploration of Resilience in the Face of Failure”  Panel Presentation, sponsored by the Bureau of Study Counsel and Harvard Alumni Association, Harvard University.

8/25/11              “What Happens when Mathematical Thinking is Sidetracked by Wishful Thinking?” Presentation to Panel on “New Challenges in Mathematical Statistics”, 58^th ISI Meeting, Dublin, Ireland.

10/14/11           “Stories of Success and Failure: Finding the Path of Most Resilience” Presentation at HAA (Harvard Alumni Association) Board of Directors’ Fall Meeting, Harvard. (joint presentation with Abigail Lipson).

10/28/11           “Statistical Education and Educating Statisticians: Producing Wine Connoisseurs and Master Winemakers.” Department of Educational Psychology, University of Minnesota.

1/18/12              “Statistical Education and Educating Statisticians: Producing Wine Connoisseurs and Master Winemakers.” Department of Statistics, University of Toronto.

4/27/12              “Statistical Education and Educating Statisticians: Producing Wine Connoisseurs and Master Winemakers.” Department of Statistics, London School of Economics.

9/28/12              “Stat 303: The Art and Practice of Teaching Statistics.”  Workshop on Supporting and Connecting New Graduate Program sin Statistics Education (celebrating 10^th Anniversary of Statistical Education Program at UM), Department of Educational Psychology, University of Minnesota.

4/9/2013            “Teach how to teach, communicate how to communicate, and learn how to learn.”  Webinar for CAUSE

                                (Consortium for the Advancement of Undergraduate Statistic Education).

5/18/13              “Are My Students Actually Learning?”   United States Conference On Teaching Statistics 2013, Raleigh-

 Durham (Research Triangle), North Carolina.

7/14/13               “Preparing Future Faculty to Assess Student Learning.” Dean Dialogue, Council of Graduate School

 Summer Workshop, San Juan.

8/4/13                  “A Time Travel with 40 Statistics Departments: Stories You Want to Hear (or Forget).” 2013 JSM, Montreal.

6/13/14               “Personalized Treatment: Sounds heavenly, but where on Earth did they find the right guinea

                             pig for me?”  Harvard Club of Cape Cod Annual Meeting, Cape Cod, Massachusetts.

6/17/14               “Leading Across Boundaries: Leadership Development for Statisticians” 2014 ICSA-KISS Applied Statistical Symposium, Portland, Oregon.

8/2/14                    “Leadership” IMS New research Conference, Cambridge, Massachusetts.

8/5/14                    “Challenges and Opportunities for Statistics in the Next 25 Years” Invited Panel, 2014 JSM, Boston.

10/18/14                 “The Four Seasons of Graduate School” ACGS (Association of Chinese Graduate Schools) International

   Forum, Tianjin, China.

3/21/15                  “From Fudan to Harvard: A collection of personal and professional stories” Lunch Speaker,

                                Fudan Fuzhong Overseas Foundation.

5/14/15                  “Three sides of the same coin:  Teaching, Research, and Learning” Intergenerational Education Symposium,

   Harvard University.

5/22/15                “Bigger Data, Deeper Learning, and Better Understanding” Ningbo University, China.

6/16/15                “Leadership Forum” Joint 24th ICSA Applied Statistics Symposium and 13th Graybill Conference, Fort

   Collins, Colorado.

8/9/ 15                   “Undergraduate Curriculum: the Front Entry to the Statistical Paradise(s)” invited Panel Discussion, 2015

   JSM, Seattle.

10/23/15               “Research Irreproducibility: Diagnoses and Treatments (Or how do we educate our students to torture data

   ethically)” 2015 Program in Graduate Education Symposium, Harvard Medical School.

12/3/15                  “Some Statistical Thoughts Inspired by Professor Julie Posselt's Research (Or:  Some GRE Questions for

   Deans and ...)” 55th Annual meeting of Council of Graduate Schools (CGS), Seattle.

12/6/15                  “How to teach and learn about Statistics (and big data) happily?” TPES (Transforming Post-Secondary

   Education in Mathematics) Meeting, Duke University.

11/11/15               “The Four Seasons of Graduate School” Inaugural meeting of Harvard 1879 Society, Harvard Shanghai

   Center.

2/23/16                  “Research Irreproducibility: Diagnoses and Treatments (Or: How to torture our data ethically?)” Harvard

   Medical School, Postdoc Seminar.

6/1/16                    “Statistical Education and Educating Statisticians: Producing Wine Connoisseurs and Master Winemakers”

                               Australia National Colloquium and Workshop on STEMS: Putting Statistics into STEM in the Age of Data,                    

                               University of Technology Sydney.

7/2/16                   “The Four Seasons of Graduate School: How Harvard Innovates” Keio University, PLGS (Program of

   Leading Graduate School).

7/20/16                  “”Three Sides of the Same Coin: Teaching, Research, and Learning” Pipelines into Biostatistics Visiting

                                Faculty Workshop  Harvard University

9/28/16                 “I Was One of You …” Population Health Science Inaugural Ph.D. Cohort Meeting, Harvard Chan School. 

12/9/16                  “Big Data, Big Desire, & Big Danger” CGS Panel on Ethics in the Era of Big Data, 56th Annual meeting

                               CGS, Washington, D. C.

12/15-16/16          “GenEd 105S: Vital statistics for life and medical sciences”  & “Scholarship in the new age: Integrating

                               research and professional development”   Excellence in Teaching Workshops — How Harvard Teaches

                               and Innovates, Hong Kong University.

12/19/16               “Data Science:  Sleepless in Big Data”  Panel on “Data Science: Who Cares?” 10^th ICSA International 

                              Conference,  Shanghai Jiao Tong University.

1/4/17                    “Hi – Harvard i-lab”   Panel on “Global Innovation in IS/IT: The Role of Innovative Entrepreneurship?”

                              Hawaii International Conference on System Science, Waikoloa, Hawaii.
1/5/17                    ““Big Data, Bigger Desire, & Biggest  Danger” Harvard Club of Hawaii, Honolulu.

1/18/17                  “Preparing Future Faculty to Teach and Assess Today and Tomorrow’s Students”  Panel presentation at

                               Asia Public Policy Forum, Sunway University, Malaysia.   

3/9/17                     “The Seven Deadly Selection Biases” Harvard Medical School, Postdoc Seminar.

6/12/17                  “Scholarship in the new age: Integrating research and professional development”, Interdisciplinary Center for Scientific Computation,  University of Heidelberg.

6/13/17                  “Collecting, Analyzing and Interpreting Statistical Evidence”,  Harvard Summer School Study Abroad,  Cité Internationale Universitaire de Paris

6/15/17                  “Probabilistic Thinking and Risk Assessment”,  Harvard Summer School Study Abroad,  Cité Internationale Universitaire de Paris

6/18/17                 “Modularizing, Magnetizing, and Multilayering -- Towards more effective science general education “

Hungarian Academy of Science,  Budapest

7/19/17                  “Modularizing, Magnetizing, and Multilayering -- Towards more effective science general education”, Summer Program in Biostatistics & Computational Biology, Department of Biostatistics, Harvard

11/8/17                  “Gaining Statistical Insights via Its Paradoxes: From Personalized Treatment (Simpson's Paradox) to 2016

Elections (The Big-Data Paradox)”  Statistics Day, Takeda, Boston.

3/5/18                     “Research Irreproducibility: Diagnoses and Treatments (Or:  how to torture our data ethically?)”

Harvard Medical School, Postdoc Seminar.

7/18/18                 “Double Your Variance, Dirtify Your Bayes, Devour Your Pufferfish, and Draw Your Kidstrogram”

Summer Program in Biostatistics & Computational Biology, Department of Biostatistics, Harvard

9/19/18                 “For the Development in the Big Data Era:  More Smart Machines or More Smart People?”

Lunch presentation to entrepreneurial delegation from Zhejiang Province, Harvard Faculty Club.

9/24/18                 “Double Your Variance, Dirtify Your Bayes, Devour Your Pufferfish, and Draw Your Kidstrogram” (Part I)

and “My Painful N-of-1 Trial (Or:  A Quest for Actionable Probability)” (Part II) Statistics Day, Takeda

4/1/19                    “Bias-Variance Tradeoff: A Fundamental Statistical Principle That Can Render You Love or Pain”, Association for Women in Mathematics and Society for Industrial and Applied Mathematics clubs, IIT

4/2/19                   “Conducting Highly Principled Data Science: A Statistician's Job and Joy”, Career Advice Seminar, IIT.

7/30/19                 “Exact Estimation with Markov Chain Monte Carlo” (Imputing Yves Atchade), JSM 2019.  Denver, Colorado.

8/1/19                   “Vision 2020: Making Impact with Statistics in the Era of Data Science”, Invited Panel Discussion. JSM 2019.  Denver, Colorado.

7/16/20                 “Understanding and Teaching the Greatest Statistical Magic: Learning About a Population of Great Many From a Sample of Very Few”  WPS Summer Training Program for Secondary School Data Science Education (Virtual).

7/31/20                 “Remote Teaching”, Panel Discussion,  WNAR Webina.

8/24/20                 “Ensuring a Healthy Data Science Ecosystem:  Pedagogical Challenges and Opportunities”.  KDD 2020

Virtual Conference, Workshop on Data Science Standards.

8/26/20                IMS-Bernoulli One-World Symposium Panel for New Researchers (Virtual)

4/29/21                 “Everything Data Science and Data Science for Everyone” ALI (Advanced Leadership Initiative) Deep Dive

on Technology Innovation and Social Impact, Harvard  (Virtual)

5/21/21                “Harvard Data Science Review: Everything Data Science and Data Science For Everyone” IMS-HBS Young

Researcher Workshop, NESS Harvard Business School (Virtual)

9/20/21                “Avoiding Data Fallacies:  From COVID-19 Vaccination to Election Prediction” LPCE (Lemann Program

on Creativity and Entrepreneurship) 101 Virtual Guest Lecture, Harvard.

10/29/22              “Statistical Paradises and Paradoxes (统计的仙境与陷阱)”  Virtual Lecture (in Chinese) for Voice of

Harvard by Harvard Chinese Students and Scholars Association (HCSSA).

11/16/22               “Communicating Data Science – Trust With Complexity” Harvard Data Science Initiative Annual

Conference Panel Discussion.

5/11/23                 “From Gaussianity to Cauchyanity: A Worthwhile Trade-off for (Ultra) High-dimensional Inference?”

Statistical Foundations of Data Science and their Applications, A conference in celebration of Jianqing Fan's 60th Birthday,  Princeton

 

 

Invited Departmental and Institutional Research Seminars:

3/3/92                    Stat., UC Berkeley.  A Generalized EM: The ECM Algorithm.

3/4/92                    Biostat., UCLA.  A Generalized EM: The ECM Algorithm.

9/17/92                  Stat., Penn. State U.  A Bayesian P-value.

9/18/92                  Stat., Virginia PI&SU.  A Generalized EM: The ECM Algorithm. 

11/16/92                Stat., Northwestern U.  A Bayesian P-value.

2/4/93                    Stat., Purdue U.  Coherent Multiple-Imputation Inference Under Incoherent Models.

2/18/93                  Biostat., UCLA.  Simulating Ratios of Normalizing Constants.

5/12/93                  Business, U. Chicago.  Simulating Ratios of Normalizing Constants and a New Property of Jeffreys’ Prior.

2/2/94                    Stat., UIUC.  Bridge Sampling and Path Sampling for Computing Normalizing Constants.

2/4/94                    Math., North. Ill. U.  Bridge Sampling and Path Sampling for Computing Normalizing Constants.

2/18/94                  Biostat., Michigan.  Bridge Sampling and Path Sampling for Computing Normalizing Constants.

4/28/94                  Demography, NORC.  Multiple-Imputation Inference with Uncongenial Sources of Input.

1/25/95                  Stat., Rutgers U.  Bridge Sampling and Path Sampling for Computing Normalizing Constants.

1/26/95                  Biostat., Columbia U.  Bridge Sampling and Path Sampling for Computing Normalizing Constants.

9/25/95                  Stat., Iowa State U.  Sometimes There is a Free (and Delicious) Lunch ......

– Augmenting Data Wisely to Speed up the EM Algorithm. 

1/17/96                  Stat., RAND.  Understanding Multiple Imputation.

1/18/96                  Stat., UCLA.  Sometimes There is a Free (and Delicious) Lunch ......

2/14/96                  Stat., Carnegie Mellon U.  AR(1), Bias, Correlation, Fisher, Fractional Derivative, James-Stein Estimator,

and Wiener Process.

3/4/96                    Biostat., Harvard U.   The EM Algorithm – An Old Folk Song Sung to a Fast New Tune.

3/6/96                    Stat., Harvard U.  Bridge Sampling – An Improvement and Extension of Importance Sampling.

3/20/96                  Stat., U. Penn.  Computing Normalizing Constants: From Importance Sampling to Bridge

Sampling to Path Sampling.

3/21/96                  ETS and Princeton ASA.  Understanding Multiple Imputation.

8/29/96                  Stat., Purdue U.  AR(1), Bias, Correlation, Fisher, Fractional Derivative, James-Stein Estimator,

and Wiener Process.

12/5/96                  Stat., Oxford U.   Inferences under Uncongeniality: Super-efficiency and Inadmissibility.

12/6/96                  Math., U. of Bath.  AR(1), Bias, Correlation, Fisher, Fractional Derivative, James-Stein Estimator, 

and Wiener.

12/9/96                  Stat., U. of Bristol.  Inferences under Uncongeniality: Super-efficiency and Inadmissibility.

12/10/96                Math., Imperial College.  Inferences under Uncongeniality: Super-efficiency and Inadmissibility.

12/11/96                Royal Stat. Soc., UK.  The EM Algorithm – An Old Folk Song Sung to a Fast New Tune.

5/7/97                    Stat., Harvard U.  AR(1), Bias, Correlation, Fisher, Fractional Derivative, James-Stein Estimator, 

and Wiener Process.

5/8/97                    Biostat., Harvard U.  The EM Algorithm and Medical Studies: A Historical Link .

11/12/97                Math., North. Ill. U.  AR(1), Bias, Correlation, Fisher, Fractional Derivative, James-Stein Estimator,

and Wiener Process.

1/22/98                  Biostat., WU Seattle.  The Art of Data Augmentation .

1/30/98                  Stat., Michigan U.  AR(1), Bias, Correlation, Fisher, Fractional Derivative, James-Stein Estimator, and Wiener Process.

2/6/98                    Math., U. Maryland Baltimore.  The EM Algorithm and Medical Studies: A Historical Link .

2/9/98                    Stat., Columbia U.  The Art of Data Augmentation.

6/4/98                    Bell Lab, Lucent Tech.   Thermodynamic Integration and Path Sampling.

7/6/98                    Stat. Engineering, NIST.  Thermodynamic Integration and Path Sampling.

11/5/98                  Stat., Ohio State U.  Cholera, EM, and Deceptively Large Samples:  A Statistical Mystery.

12/17/98                Math., Boston U.  Cholera, EM, and Deceptively Large Samples:  A Statistical Mystery.

5/3/99                    Pub. Health, Odense U.  Applications of Multiple Imputation in Medical Studies: From AIDS to NHANES.

5/10/99                  Stat., Iowa State U.  Improving Perfect Simulation.

5/19/99                  Stat., Columbia U.  Improving Perfect Simulation.

7/8/99                    Stat., W. Ontario U.  Improving Perfect Simulation.

11/12/99                Stat., Harvard U.  Improving Perfect Simulation.

11/15/99                Stat., Harvard U.  Statistical Relative Information in Genetic Hypothesis Testing.

9/5/00                    Biostat., Yale U.                  Cholera, EM, and Deceptively Large Samples: A Statistical Mystery.

9/6/00                    Stat., Yale U.  Single Observation Unbiased Priors.

11/9/00                  Stat., Hong Kong (HK) U. Single Observation Unbiased Priors.

11/10/00                Math., HKU of Sci.&Tech.  Self-consistency and Wavelet Shrinkage with Irregular Designs.

12/12/00                Math., Chinese U. of HK.  Single Observation Unbiased Priors.

4/4/01                    Business, U. Chicago.  Self-consistency and Wavelet Shrinkage with Irregular Designs.

4/6/01                    Biostat., UIC.  Cholera, EM, and Deceptively Large Samples: A Statistical Mystery.

1/9/02                    Biostat., Harvard U.  Warp Bridge Sampling.

12/10/02                Math., Cambridge U.  Perfect Sampling, Antithetic Coupling, and Antihype Fractals.

12/11/02                Royal Stat. Soc., UK.  A Theory of Statistical Models for Monte Carlo Integration.

12/14/02                Math., Lancaster U.  Cooking SOUP: Single Observation Unbiased Prior.

3/17/03                  ISDS, Duke U.   Inference with Monte Carlo Data: The Bayesian Paradox?

3/19/03                  Biostat., UNC Chapel Hill.  Statistical Information in Genetic Studies.

3/21/03                  Stat., NC State.  Self-consistency and Wavelet Shrinkage with Irregular Designs.

4/22/03                  Stat., U. Penn.  Inference with Monte Carlo Data: The Bayesian Paradox?

4/24/03                  Stat., Penn. State.  Inference with Monte Carlo Data: The Bayesian Paradox?

11/3/03                  Health Study, U. Chicago.  (Data) Size Does Matter, But You Might Be in for a Surprise ...

4/1/04                    Stat., CMU.  (Data) Size Does Matter, But You Might Be in for a Surprise ...

4/21/04                  Stat., Rutgers U.  From Unit Root to Stein Estimator to Fisher’s k-statistics

– If You Have a Moment, I Can Tell You More ...

6/25/04                  Stat., Los Alamos.  Inference with Monte Carlo Data: A Paradox of “Knowing Too Much”?

– (Or “How to cure our schizophrenia?”)

10/7/04                  Stat., U. Florida.  Inference with Monte Carlo Data: A Paradox of “Knowing Too Much”?

– (Or “How to cure our schizophrenia?”)

2/16/05                  Government, Harvard.  (Data) Size Does Matter, But You Might Be in for a Surprise ...

2/18/05                  ISDS, Duke U.  (Data) Size Does Matter, But You Might Be in for a Surprise ...

3/30/05                  Health Stat Seminar Series, Boston U.  (Data) Size Does Matter, But You Might Be in for a Surprise ...

6/24/05                  National Sun Yat-Sen University, College of Management & Applied Math Dept.  A Tutorial on Markov

Chain Monte Carlo.

6/27/05                  National Chengchi University.  A Tutorial on Markov Chain Monte Carlo.

6/29/05                  Institute of Statistics, Academia Sinica, 2005 Statistics Summer Camp.  How "Crude" is Harvard President's

Calculation?

6/30/05                  National Health Research Institutes, Division of Biostatistics and Bioinformatics, Taiwan.  Quantifying

Relative Incomplete Information for Hypothesis Testing in Statistical and Genetic Studies.

7/4/05                    Stat, Hong Kong U.   From Unit Root to Stein Estimator to Fisher’s k-statistics

– If You Have a Moment, I Can Tell You More ...

7/15/05                  Stat, Hong Kong U. (Data) Size Does Matter, But You Might Be in for a Surprise ...

9/23/05                  Math, Fudan U.   Markov Chain Monte Carlo:  A Workhorse for Modern Scientific Computation

11/17/05                 Stat, UCLA.  Espousing Modern Computation with Classical Statistics: Sufficiency, Ancillarity and A New

Generation of MCMC.

11/18/05                 Stat & Actuarial Sci, Simon Fraser U.  Espousing Modern Computation with Classical Statistics:

Sufficiency, Ancillarity and a New Generation of MCMC.

3/10/06                  Math & Stat, University of Montreal.  How "Crude" is Harvard President's Calculation?

7/10/06                  Math & Stat, U. New South Wales (UNSW), Sydney.  Life becomes more colorful when you know EM,

Bayes and Wavelets …

7/12/06                  Math Science Inst., ANU Life becomes more colorful when you know EM, Bayes and Wavelets …

8/3/06                    Institute of Statistics, Academic Sinica, Taiwan.  Life becomes more colorful when you know EM, Bayes,

and Wavelets …

9/20/06                  Math, Smith College.  The Full Monte Carlo: A Live Performance.

3/26/07                  Washington Statistical Society, Washington, D.C.  (Data) Size Does Matter, But You Might Be In for a Surprise...

4/27/07                  Stat., U. Georgia.  How ‘Crude’ was Harvard Ex-President’s Calculation?

6/30/07                  Business Mgmt., Sun Yat-Sun University, Taiwan.  Bayesian Statistics and Computation: An Advanced Tool for Quantitative Decision Making.

7/10/07                  Stat. & Actuarial Sci., U. Hong Kong.  The Full Monte Carlo: A Live Performance.

7/12/07                  Inst. of Stats. & Comp. Intell., United International Coll., Zhuhai, China.    Bayesian Statistics and Computation: An Advanced Tool for Quantitative Decision Making.

9/17/07                  Stats., Harvard U.  A Semi-Theoretician's Mid-Day Confession: The True meaning of i.i.d. in (Applied) Statistics.

10/17/07               Math. & Stats., U. Missouri, Kansas City.  Self-Rao-Blackwellization: Bringing the Best Out of ‘Bad’ Data or ‘Bad’ Procedures.

10/17/07               ASA Kansas-West Missouri Chapter Fall Mtg., U. Kansas.  A Semi-Theoretician's Mid-Day Confession: The True meaning of i.i.d. in (Applied) Statistics.

11/2/07                  Stats., Carnegie Mellon U.  Self-Rao-Blackwellization: Bringing the Best Out of ‘Bad’ Data or ‘Bad’ Procedures.

11/16/07               Stats., U. California, Irvine.  A Semi-Theoretician's Mid-Day Confession: The True meaning of i.i.d. in (Applied) Statistics.

1/16/08                  Stats, Wharton, Penn.  Self-consistency: A General Recipe for Semi-parametric and Non-parametric Estimation with Incomplete and Irregularly Spaced Data

1/18/08                  ASA Philadelphia Chapter, Horsham, Pennsylvania.  A Semi-Theoretician's Mid-Day Confession: The True meaning of i.i.d. in (Applied) Statistics.

1/25/08                  Stats, GWU.  A Semi-Theoretician's Mid-Day Confession: The True meaning of i.i.d. in (Applied) Statistics.

2/21/08                  Pub. Health and Prev. Med., Oregon Health Sciences U. Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies.

2/21/08                  ASA Oregon Chapter. A Semi-Theoretician's Mid-Day Confession: The True meaning of i.i.d. in (Applied) Statistics.

3/6/08                    Washington Statistical Society, Washington, D.C.  Statistics Can Lie But Can Also Correct for Lies:
Reducing Response Bias in NLAAS via Bayesian Imputation.

3/7/08                    Biostat Branch, National Cancer Institute.  Disparities in Defining Disparities: Statistical Conceptual Frameworks

3/13/08                  ASA Central Indiana Chapter. A Semi-Theoretician's Mid-Day Confession: The True meaning of i.i.d. in (Applied) Statistics.

4/11/08                  School of Pub. Health, UIC.  A Semi-Theoretician's Mid-Day Confession: The True meaning of i.i.d. in (Applied) Statistics.

7/28/08                  School of Math and Stat, UNSW, Sydney.  Disparities in Defining Disparities: Statistical Conceptual Frameworks

7/29/08                  School of Applied Math and Statistics, U of Wollongong.  Statistics Can Lie But Can Also Correct for Lies: Reducing Response Bias in NLAAS via Bayesian Imputation.

9/18/08                  Math., University of Georgia. Self-consistency: A General Recipe for Semi-parametric and Non-parametric Estimation with Incomplete and Irregularly Spaced Data.

10/15/08               Stats, GWU.  Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies.

2/13/09                  Stats, Duke.  To Center or Not to Center, That Is Not the Question – An Ancillary-Sufficient Interweaving Strategy for Boosting MCMC Efficiency.

2/27/09                  Stats, U of Kentucky.  Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies

4/11/09                  Stat, Columbia.  To Center or Not to Center, That Is Not the Question – An Ancillary-Sufficient Interweaving Strategy for Boosting MCMC Efficiency.

9/18/09                  Stat & Prob., Michigan State Univ.  To Center or Not to Center, That Is Not the Question – An Ancillary-Sufficient Interweaving Strategy for Boosting MCMC Efficiency.

10/29/09               Stat, U South Carolina.   Self-consistency: A General Recipe for Semi-parametric and Non-parametric Estimation with Incomplete and Irregularly Spaced Data.

12/18/09               Google, Mountain View.  Trivial Mathematics but Deep Statistics: Simpson’s Paradox and Its Impact on Your Life

1/13/10                  Stat, Cornell U.  30 Years of Bootstrap and Multiple Imputation: Joint Replications versus Conditional Replications.

2/17/10                  Math, U. of Central Florida.  Self-consistency: A General Recipe for Semi-parametric and Non-parametric Estimation with Incomplete and Irregularly Spaced Data

2/18/10                  Stat., U of Florida. 30 Years of Bootstrap and Multiple Imputation: Joint Replications versus Conditional Replications.

3/27/10                  Stat, Orebro U., Sweden.  Automated Bias-variance Trade-off: Intuitive Inadmissibility or Inadmissible Intuition?

4/2/10                    Stat, Business School, Temple U. To Center or Not to Center, That Is Not the Question – An Ancillary-Sufficient Interweaving Strategy for Boosting MCMC Efficiency.

10/6/10                  Stat, U of British Columbia.  30 Years of Bootstrap and Multiple Imputation: Joint Replications versus Conditional Replications.

10/26/10               Stat., Stanford U. A seminal trio: Applying Efron's (1967) self-consistency principle to Donoho and Johnstone's (1994) wavelets and Tibshirani's (1996) LASSO regressions with incomplete and irregularly spaced data

10/27/10               Math Organization, Stanford U.  The Full Monte Carlo: A Live Performance

11/10/10               Stat, Rutgers U.  What's the H in H-likelihood: A Holy Grail or an Achilles' Heel?

11/19/10               Sheldon B. Lubar Business School, University of Wisconsin-Milwaukee: Trivial Mathematics but Deep Statistics: Simpson’s Paradox and Its Impact on Your Life

1/28/11                  Math & Stat, Arizona State U: Trivial Mathematics but Deep Statistics: Simpson’s Paradox and Its Impact on Your Life

2/8/11                    Biostat & Bioinformatics, UCSD: Gene-Environment Interaction, Automated Bias-Variance Trade-off, & Nano-Project Ph.D. Qualifying Exams

2/22/11                  Management Science and Statistics, U of Taxes at San Antonio: Trivial Mathematics but Deep Statistics: Simpson’s Paradox and Its Impact on Your Life

2/24/11                  Stat, Texas A&M U: What's the H in H-likelihood: A Holy Grail or an Achilles' Heel?

7/13/11                  Center for Devices and Radiological Health, FDA:  Simpson’s Paradox and Its Impact on Your Life

7/14/11                  Center for Devices and Radiological Health, FDA:  Gene-Environment Interaction, Automated Bias-Variance Trade-off, & Nano-Project Ph.D. Qualifying Exams

8/26/11                  Stat, UCL:  Statistical Inception for the MCMC Dream: The kick is in the residual (augmentation)!

9/1/11                    Institut Henri Poincaré, Université Paris-Dauphine. Statistical Inception for the MCMC Dream: The kick is in the residual (augmentation)!

10/28/11               Biostat, U of Minnesota. Gene-Environment Interaction, Automated Bias-Variance Trade-off, & Nano-Project Ph.D. Qualifying Exams

12/6/11                  Industrial and Systems Engineering, U of Southern California. Machine Learning with Human Intelligence: Principled Corner Cutting (PC²)

1/19/12                  Stat, U. Toronto.  Statistical Inception for the MCMC Dream: The kick is in the residual (augmentation)!

2/24/12                  Department of Information, Operations and Management Sciences, Business School, NYU. Trivial Mathematics but Deep Statistics: Simpson’s Paradox and Its Impact on Your Life

3/4/12                    Stat, Berkeley. I Got More Data, My Model is More Refined, But My Estimator is Getting Worse! Am I Just Dumb?

4/26/12                  Judge School of Business, University of Cambridge. I Got More Data, My Model is More Refined, But My Estimator is Getting Worse! Am I Just Dumb?

4/30/12                  Biostatistics, Brown. Gene-Environment Interaction, Automated Bias-Variance Trade-off, & Nano-Project Ph.D. Qualifying Exams

1/25/13                  Purdue Statistics Spring Research Colloquium. I Got More Data, My Model is More Refined, But My Estimator is Getting Worse! Am I Just Dumb?

6/7/14                   Mathematics, Imperial College of London. Being an informed Bayesian: Assessing prior informativeness and prior--likelihood conflict.

1/23/15                  Harvard Shanghai Center. Personalized Treatment: Sounds heavenly, but where on Earth did they find the

right guinea pig for me

4/21/15                  Baidu, Inc., Beijing. Bigger Data, Deeper Learning, and Better Understanding.

9/30/15                  Center for Improving Methods for Quantitative Policy Research, Northwestern University. Is it a computing

                                                algorithm or a statistical procedure: Can you tell or do you care?

11/13/15               Quantitative Research Methodology for the Social and Behavioral Sciences workshop, University of Chicago. I Got More Data, My Model is More Refined, But My Estimator is Getting Worse! Am I Just Dumb?

2/16/17                 School of Mathematical and Statistical Science, Arizona State University.  From Euler to Clinton: An Unexpected Statistical Journey (Or: Size Does Matter, But You Might Be in for a Surprise…)

3/1/17                   Department of Statistics, University of Pittsburgh.  Statistical Paradises & Paradoxes in Big Data

3/3/17                   Department of Statistics, University of Virginia. From Euler to Clinton: An Unexpected Statistical Journey

                                            (Or: Size Does Matter, But You Might Be in for a Surprise…)

10/13/17               Department of Statistics, George Washington University.   Dissecting Multiple Imputation from a Multi-

phase Inference Perspective: What happens when God’s, Imputer’s and Analyst’s models are uncongenial?

2/16/18                 Department of Mathematics and Statistic, McGill University.  The Law of Large Populations: The return of

the long-ignored N and how it can affect our 2020 vision.

3/12/18                Judge Business School, Cambridge University. The Law of Large Populations:  The return of the

                                                long-ignored N and how it can affect our 2020 vision.

3/19/18                 Department of Mathematics and Statistics, Cobly College. The Law of Large Populations:  The return of the

                                                long-ignored N and how it can affect our 2020 vision.

4/25/18                 Oxford PPE (Philosophy, Politics, and Economics) Society, University of Oxford. How Small Are Our Big

Data: Turning the 2016 Surprise into a 2020 Vision

5/15/18                 FDA Statistical Association (FDASA), Silver Spring, MD.   Double Your Variance, Dirtify Your Bayes,

                                                Devour Your Pufferfish, Draw Your Kidstogram.

5/30/18                 Biostatistics Program, Fred Hutch, Seattle.   Statistical Paradises and Paradoxes in Big Data (I):

                                                Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election.

5/31/18                 Department of Biostatistics, University of Washington, Seattle.   Statistical Paradises and Paradoxes in Big

                                                Data (II): Multi-resolution Inference, Simpson's paradox, and Individualized Treatments

6/22/18                 Statistics Canada.  The return of the long-ignored N and how it can affect our 2020 vision.

9/7/18                   Division of Biostatistics, University of Washington in St. Louis. How Small Are Our Big Data: Turning the

2016 Surprise into a 2020 Vision

11/26/18               Department of Mathematics, Imperial College London. How Small Are Our Big Data: Turning the 2016

Surprise into a 2020 Vision

2/18/19                  Department of Operations Research and Financial Engineering, Princeton University.  Is It a Computing

Algorithm or a Statistical Procedure – Can You Tell or Should You Care?

4/2/19                    Department of Applied Mathematics, IIT. Is It a Computing Algorithm or a Statistical Procedure – Can You

Tell or Should You Care?

5/14/19                 Departments of Statistics and Biostatistics ,  UCLA. How Small Are Our Big Data: Turning the 2016

Surprise into a 2020 Vision 

7/10/19                 Census Bureau, Suitland, MD.   Data Science:  What it is Not?

7/11/19                 Census Bureau, Suitland, MD.  A Trio of Inference Problems That Could Win You a Nobel Prize in

Statistics (if you help fund it)

10/7/19                 Judge Business School, University of Cambridge. Data Science: What is it not?

10/8/19                 Department of Biostatistics, University of Cambridge. A Trio of Inference Problems That Could Win You a

Nobel Prize in Statistics (if you help fund it)

10/11/19               Egenis, The Centre for the Study of Life Science, University of Exeter.    Dissecting Multiple Imputation

From a Multi-phase Inference Perspective: What happens when God’s, Imputer’s and Analyst’s models are uncongenial?

10/11/19               IDSAI (Institute of Data Science and AI) and Egenis, University of Exeter, England. 

AI, Beatles, and Elections - A Nano Tour of Data Science

10/16/19               Department of Statistical and Actuarial Sciences, Western Ontario University.  Building Deep Statistical

Thinking for Data Science 2020: Privacy Protected Census, Gerrymandering, and Election.

10/29/19               BUSCASA (BU Student Chapter of ASA), World Statistics Day Presentation: Astrostatistics: The 

Intersection of Statistics and Outer Space (jointly with Aneta Siemiginowska & Vinay L. Kashyap)

11/13/19               Applied Statistics Workshop, Government Department, Harvard. 2020 Election and Privacy-Protected

Census: Data Quantity vs. Quality & Data Privacy vs. Utility

11/13/19               IDS 190, Graduate Research Program in Statistics, MIT.  Artificial Bayesian Monte Carlo Integration:  A

Practical Resolution to the Bayesian (Normalizing Constant) Paradox.

2/26/20                 Biostatistics, Memorial Sloan Kattering Cancer Center.  2020: A Very Busy Year for Data Scientists –

                                              Quantity vs. Quality and Privacy vs. Utility

2/27/20                 Policy Development Seminar, Economic Analysis and Policy Division, United Nations. Quantity vs. Quality

and Privacy vs. Utility Emerging challenges and opportunities for data scientists

9/24/20                 Booth School of Business, University of Chicago (Virtual). Pushing Large-p-Small-n to Infinite-p-Zero-n: 

A Multi-resolution Theory for Individualized Predictions and for A World without Bias-Variance Trade-off.

9/25/20                Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston

(Virtual.) Pushing Large-p-Small-n to Infinite-p-Zero-n: A Multi-resolution Theory for Individualized

Predictions and for A World Without Bias-Variance Trade-off

10/28/20               Economic Club Symposium (Virtual), New College of Florida.  I got more data, my model is more refined,

but my estimator is getting worse! Am I just dumb?

11/18/20               Department of Statistics, University of Wisconsin at Madison (Virtual). A Multi-resolution Theory for

Approximating Infinite-p-Zero-n: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Trade-off

11/20/20               Quantitative Methods Workshop, University of Chicago (Virtual).  Statistical Learning with Low Resolution

Information:  There is No Free Lunch

6/3/21                  Cambridge BSU Lectures in Biomedical Data Science,  University of Cambridge (June 3,  Virtual)

Personalized Treatment: Sounds Heavenly, but Where on Earth Did They Find My Guinea Pigs?

7/6/21                    International Research Training Group (IRTG), University Hospital RWTH Aachen, Germany (Virtual)

From Election Prediction to COVID-19 Vaccination: How Small Are Our Big Data?

7/7/21                   Data Science Seminar and PHYSTAT seminar, CERN. (Virtual)  From COVID-19 Testing to Election

Prediction: How Small Are Our Big Data?

9/15/21                 Special Household Pulse Survey Group Meeting, US Census Bureau (Virtual) Data Defect Correlation:  A

Unified Quality Metric for Probabilistic and Non-probabilistic Samples.

10/20/21               Department of Biostatistics, MD Anderson, Houston (Virtual Visiting Guest Lecture) Bayesian Inference

with Incomplete Likelihood or Prior: Employing Quick-and-Dirty Bayes Theorems and Prior Impact Assessments

11/10/21               Mathematics Department, United States Naval Academy (Virtual) Let’s train more Sherlock Holmes's of and

for Data Science: Data Minding before Data Mining

11/18/21               Department of Statistics, University of Minnesota (Virtual) Conducting Highly Principled Data Science: A

Statistician's Job and Joy

2/28/22                 Department of Statistical Science, University of Cape Town (Virtual) Bayesian Inference with Incomplete

Likelihood or Prior: Employing Quick-and-Dirty Bayes Theorems and Prior Impact Assessments

3/9/22                   International Research Training Group (IRTG), University Hospital RWTH Aachen, Germany (Virtual)

Personalized Treatment: Sounds Heavenly, But Where on Earth Did They Find My Guinea Pigs?

3/16/22                 Bureau of Transportation Statistics, Department of Transportation, Washington DC(Virtual) AI, Data

Science, and Machine Learning: Are They Just Catchier Names for Statistics?

4/5/22                   Department of Physics, Boston University. From Election Polling to COVID-19 Surveys: How Small Are

Our Big Data?

8/31/22                 Department of Statistics and Data Science, Wharton School, University of Pennsylvania (Virtual). 

Personalized Treatments: Sounds Heavenly, but Where on Earth Did They Find My Guinea Pigs?

10/11/22               Department of Operations Research and Financial Engineering, Princeton University. Pushing Large-p-

Small-n to Infinite-p-Zero-n: A Multi-resolution Theory for Individualized Predictions and for A World Without Bias-Variance Trade-off.

10/13/22               Department of Biostatistics, UNC, Chapel Hill (Virtual).   Bayesian Inference with Incomplete Likelihood or

Prior: Employing Quick-and-Dirty Bayes Theorems and Prior Impact Assessments

10/17/22               Chalmers AI Research Centre, ​Chalmers University of Technology.  A Multi-resolution Theory for

Approximating Infinite-p-Zero-n: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Tradeoff​.

​ 11/9/22                Departments of Statistics and Political Science, University of Wisconsin at Madison.   Privacy, Data

Privacy, and Differential Privacy

12/9/22                 Symposium on Statistics and Risk Management, Department of Statistics, Chinese University of Hong Kong

(Virtual) Statistical Learning with Low Resolution Information:  There is No Free Lunch

12/15/22               International Roundtable on Computational Social Science, The Institute for Analytical Sociology,  

Linköping University.  Privacy, Data Privacy, and Differential Privacy

6/1/23                 Biomedical Data Science Seminar, Stanford University.  Privacy, Data Privacy, and Differential Privacy

10/6/23               Committee on Quantitative Methods, Univ of Chicago.  Privacy, Data Privacy, and Differential Privacy

11/17/23             Department of Statistical Science, Padova University.  Privacy, Data Privacy, and Differential Privacy

12/14/23             Australia Bureau of Statistics, Canberra.  Miniaturizing Data Defect Correlation: A Versatile Strategy for

             Handling Non-Probability Samples

 

 

 

 

 

Research Conference Presentations:

8/8/89                    1989 JSM, Washington, DC.  Obtaining Asymptotic Variance-Covariance Matrices by the EM Algorithm.

8/6/90                    1990 JSM, Anaheim, CA. Likelihood Ratio Tests with Multiply-Imputed Data Sets.

8/10/91                  1991 JSM, Atlanta, GA.  IPF for Contingency Tables with Missing Data via the ECM Algorithm.

6/16/92                  5^th Purdue International Symposium.  (A) Bayesian P-value.

8/10/92                  1992 JSM, Boston, MA.  EM: A Bibliographical Review.

5/15/93                  3^rd ICSA App. Stat. Symp., Plymouth Meeting, PA.  Deterministic and Stochastic Iterative Algorithms

for Missing Data Computations.

5/17/93                  1993 TIMS/ORSA J. National Meeting, Chicago.  Deterministic and Stochastic Iterative Algorithms for

Missing Data Computations.

8/4/93                    1st IMS North American New Researchers’ Meeting, UC Berkeley, CA.  Jaccard’s Measure with Incomplete

Observations.

8/9/93                    1993 JSM, San Francisco, CA.  Correcting Reporting Delays in Surveillance Data by Multiple imputation

with Application to AIDS Surveillance.

12/17/93                2^nd ICSA Conf. and Taipei Int. Stat. Symp., Taipei.  Bridge Sampling and Path Sampling for Computing

Normalizing Constants.

4/30/94                  4^th ICSA App. Stat. Symp., Rockville, MD.  Bayesian Model Diagnosis via Posterior Predictive

Probabilities.

8/15/94                  1994 JSM, Toronto, Ontario.  Single Observation Unbiased Priors.

7/6/95                    2^nd IMS North American New Researchers’ Meeting, Queens U., Kingston.  Sometimes There is a Free (and

Delicious) Lunch......

8/13/95                  1995 JSM, Orlando, FL.  Speeding the Referee Process.

8/20/95                  3^rd ICSA Conf.,  Beijing.  The Art of Data Augmentation.

8/4/96                    1996 JSM, Chicago, IL.  Posterior Predictive Assessment: An Overview.

5/31/97                  7^th ICSA App. Stat. Symp., Rutgers, NJ.  The EM Algorithm and Medical Studies: A Historical Link. 

11/11/97                1997 American Public Health Assoc. Meeting, Indianapolis.  The EM Algorithm and Medical Studies: A

Historical Link.

6/7/98                    8^th ICSA App. Stat. Symp., New London, CT.  Simulation via Markov chain Monte Carlo: A Before-lunch

Tutorial.

8/16/98                  1998 Taipei International Stat. Symp.  The EM Algorithm and Medical Studies: A Historical Link.

8/21/98                  4^th ICSA Conf.,  Kunming.  The EM Algorithm and Medical Studies: A Historical Link.

5/11/99                  1999 Danish Soc. for Theoretical Stat.  Improving Perfect Simulation:  Multi-stage Backward Coupling and

Parallel Antithetic Coupling.

6/20/99                  9^th ICSA App. Stat. Symp., Georgetown, D.C.  Politically Incorrect Suggestions for (Chinese) Young

Faculty.

8/10/99                  1999 JSM, Baltimore, MD.  Improving Perfect Simulation: Multi-stage Backward Coupling and Parallel

Antithetic Coupling.

8/18/99                  52^nd ISI Session, Helsinki.  Irregular Designs for Wavelet Regression:  A Data Augmentation Approach.  

10/30/99                Int. Conf. on Survey Nonresponse , Portland, Oregon.  A Congenial Overview of Inferences with Partially

Imputed Data Sets Under Uncongeniality.

6/1/00                    ISBA 6^th World Meeting, Crete.  Parallel Antithetic Coupling for Perfect Bayesian Simulation.

6/9/00                    10^th ICSA App. Stat. Symp., Chicago, IL.  Statistical Information in Genetic Studies.

8/15/00                  2000 JSM, Indianapolis, IN.  A Non-interpolation Based Method for Wavelet Shrinkage with Incomplete

Designs.

11/28/00                4^th Int. Conf. on Monte Carlo and Quasi-Monte Carlo, Hong Kong.  Perfect Simulation: Triumphs, Tricks

and Trials.

8/9/01                    2001 JSM, Atlanta, GA.  Warp Bridge Sampling for Likelihood and Bayesian Computation.

4/27/02                  16^th New England Stat. Symp., Yale.  Warp Bridge Sampling.

6/7/02                    12^th ICSA App. Stat. Symp., Plymouth Meeting, PA.  Sensitivity Under Uncongeniality: A Hybrid

Framework with Illustration.

7/28/02                  65^th IMS Annual Meeting, Banff.  Antithetic Coupling for Perfect Simulation:  Progress and Problems.

7/30/02                  Royal Statistical Society Res. Section Meeting, Banff.  Discussant of Brooks et. al.

8/11/02                  2002 JSM, New York, NY.  Discussant for the invited session on “Economics and Environmental Statistics”.

12/20/02                ISM Meeting on MCMC, Tokyo.  Inference with Monte Carlo Data:  The Problem of “Knowing Too Much”.

9/19/03                  NBER/NSF Time Series, Chicago   From Unit Root to Stein Estimator to Fisher’s k-statistics  – If You Have

a Moment, I Can Tell You More ...

3/29/04                  2004 ENAR/IMS Conf., Pittsburgh, PA.  A Bayesian Framework for Quantifying Incomplete Information in Statistical and Genetic Hypothesis Testing.

5/15/04                  5^th International Indian Statistical Association Meeting,  Athens, GA.  (Data) Size Does Matter, But You Might Be in for a Surprise ... 

6/7/04                    13^th ICSA App. Stat. Symp., San Diego, CA.  Combining Curves (Almost) Bayesianly: with Applications to Public Health Patient Registries.

6/29/04                  2004 WNAR/IMS Conf., Albuquerque, NM.  A Mutant Gibbs Sampler: Incompatibility, Instability, and

Nonidentifiability Redeemed. 

3/22/05                  2005 ICASSP Conf., Philadelphia, PA.  A Self-Consistent Wavelet Method for Denoising Images

with Missing Pixels.

4/23/05                  19^th New England Stat. Symp., U. Connecticut.  How “Crude” is Harvard President’s Calculation? 

6/2/05                    4^th Graybill Conference, Fort Collins, CO.  Quantifying Relative Incomplete Information for Hypothesis

Testing in Statistical and Genetic Studies.

6/25/05                  The 2005 Joint Statistical Conference in Taiwan.  (Data) Size Does Matter, But You Might Be in for a Surprise ...

8/8/05                    2005 JSM, Minneapolis, MN.  A Nonlinear Hierarchical Model for Estimating Prevalence Rates with

Small Sample.

10/27/05                13^th Temple-Merck Conference, Plymouth, PA. .  (Data) Size Does Matter, But You Might Be in

for a Surprise ... 

7/4/06                    2006 Australia Statistical Association/New Zealand Statistical Association Conference, Auckland, New Zealand.   Espousing Modern Computation with Classical Statistics: Sufficiency, Ancillarity and A New Generation of MCMC.

8/10/06                  2006 JSM, Seattle, WA.   Espousing Modern Computation with Classical Statistics: Sufficiency, Ancillarity and A New Generation of MCMC.

4/17/07                  CDC 11th Biennial Symp. on Stat. Methods, Atlanta.  Estimating Marginal and Conditional Disparities:  A Semi-Parametric Approach.

6/5/07                    Behav. Health Serv. Res. Interest Group Mtg., Orlando.  Opportunities for Conceptualizing Health Disparities: Dealing with the Ambiguities of the IOM’s Definition for Behavioral Healthcare Disparity – A Statistical Conceptual Framework.

6/18/07                  Symp. on Recent Develop. of Stats. in Biol. Sci’s., Nat'l Health Res. Inst., ZhuNan, Taiwan.  Disparities in Measuring Disparities in Health Care: A Statistical Conceptual Framework.

7/30/07                  2007 JSM, Salt Lake City, UT.  Dealing with Review Efficiency: A Practical Bayesian Approach (Roundtable).

7/31/07                  2007 JSM, Salt Lake City, UT.  Statistics and Lies: Correcting Questionnaire Ordering Effect via Multiple Imputation.

9/15/07                  2007 MBER-NSF Time Series Conf., Iowa City, IA.  Espousing Classical Statistics with Modern Computation: Sufficiency, Ancillarity and An Interweaving Generation of MCMC.

10/27/07               50^th Anniversary Celebration & Symposium, Stats., Harvard U.  When EM Cannot Handle It, There is Self-Rao-Blackwellization.

5/2/08                    SBIES (Seminar on Bayesian Inference in Econ & Stat), Chicago. A Bayesian Framework for Quantifying Incomplete Information in Statistical and Genetic Hypothesis Testing.

6/6/08                    2008 ICSA Applied Statistics Symposium, Piscataway, NJ. Statistics Can Lie But Can Also Correct for Lies: Reducing Response Bias in NLAAS via Bayesian Imputation.

7/2/08                    First ISBS (International Symposium on Biopharmaceutical Statistics), Shanghai, China. Quantifying Incomplete Information for Hypothesis Testing in Statistical and Genetic Studies.

6/29/09                  1^st IMS-APR Meeting, Seoul,  Korea. Self-consistency: A General Recipe for Semi-parametric and Non-parametric Estimation with Incomplete Data.

7/4/09                    International Biostatistics Research Conference, Hefei, China.  Ranking with Uncertainty:  AIDS Reporting Delay in the US Cities.

8/2/09                    2009 JSM, Washington, DC.  Power-Shrinkage: An Alternative Method for Dealing with Excessive Weights

12/2/09                  30 Years of Bootstrap and Recent Advances in Statistics, Rutgers U, NJ.  30 Years of Bootstrap and Multiple Imputation:  Joint Replications versus Conditional Replications

1/7/10                    2010 AAS (America Astronomy Society) Annual Meeting, Washington, DC.  A Statistician's View of Upcoming Grand Challenges

1/21/10                  The 8^th International Conference on Health Policy Statistics, Washington, DC.  What Happens When Imputation Model and Analysis Procedure Are Uncongenial?

2/19/10                  2010 Florida Chapter ASA Annual Meeting, Tallahassee, FL.  To Center or Not to Center, That Is Not the Question – An Ancillary-Sufficient Interweaving Strategy for Boosting MCMC Efficiency

2/25/10                  Harvard Catalyst Disparities Measurement Meeting, Harvard Medical School.  Rethinking Disparity Measure: Does "Fair" Equal "Equal"?

3/19/10                  Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger, U of Texas at San Antonio.  Automated Bias-variance Trade-off: Intuitive Inadmissibility or Inadmissible Intuition?

6/9/10                    Ninth Valencia International Meeting on Bayesian Statistics and the 2010 World Meeting of the International Society for Bayesian Analysis, Benidorm (Alicante), Spain.  What's the H in H-likelihood: A Holy Grail or an Achilles' Heel?

6/25/10                  2010 SER (Society for Epidemiologic Research) Conference, Seattle, Washington.  Rethinking Disparity Measure: Does “Fair” Equal “Equal”?

7/5/10                    Probability and Statistics: An International Conference in Honor of P.L. Hsu’s 100^th birthday, Beijing, China.  What's the H in H-likelihood: A Holy Grail or an Achilles' Heel?

7/9/10                    International Symposium on Business and Industry Statistics, Portoroz, Slovenia.  A Semi-Theoretician's Mid-Day Confession: The True Meaning of i.i.d. in (Applied) Statistics

7/19/10                  2010 Summer Conference on Global Health and International Relations, Fudan University, Shanghai.  Simpson’s Paradox and “Hong-Kong Shenzhen” Paradox:  The Importance of Understanding Statistical Phenomena and Cultural  Differences in Global Health Research.

12/18/10               “Borrowing Strength: Theory Powering Applications”  In honor of Lawrence Brown’s 70^th Birthday, Department of Statistics,  Wharton School, University of Pennsylvania.  Simpson’s Paradox Before and After the Bar.

4/29/11                  2011 SBIES (Seminar on Bayesian Inference in Economics and Statistics), Washington U. at St. Louis.  How informative is my time series?

8/3/11                    2011 JSM, Miami Beach, FL.  Let's Practice What We Preach: Likelihood Methods for Monte Carlo Data

4/3/12                    2012 ENAR, Washington DC. Why Are There Multiple Hypothesis Testing Combining Rules for Multiply Imputed Data Sets?

6/15/12                  IMS/ASA Spring Research Conference, Harvard.  Statistical Computation and Computational Statistics: An Interweaving Perspective.

8/1/12                    2012 JSM, San Diego, CA. Bayesian Friends with Benefits: Partial Shrinkage.

7/25 /13               2013 European Meeting of Statisticians, Budapest.  Being an informed Bayesian: Assessing prior

informativeness and prior--likelihood conflict.

8/2/13                    ICSA Canadian Chapter Symposium, Toronto.  The potential and perils of preprocessing: Explore distributed sufficiency.

8/6/13                    2013 JSM, Montreal. What Statistical Problems Are Not Missing-Data Problems?

12/19/13               CUHK Symposium, Hong Kong. Resolving Simpson’s Paradox is a Piece of Fruit.

4/21/14                 Boston Colloquium for Philosophy of Science. Boston University. Multi-Resolution Inference: An

Engineering (Engineered?) Foundation of Statistical Inference.

6/2/2014               American Astrophysics Society Annual Meeting, Boston.  The Full Monte Carlo: A Live Performance with Stars.

6/17/14                 2014 ICSA-KISS Applied Statistical Symposium, Portland, Oregon. Stat Wars Episode IV: A New Hope

(For Objective Inference).

4/22/15                  10^th IACHEC (International Astronomical Consortium for High-Energy Calibration) Conference, Beijing. A

Log-Normal Linear Regression Approach for (Simultaneously) Adjusting Attributes.

7/2/15                    The Fifth IMS-China International Conference on Statistics and Probability, Kunming, China. Before

Individualized Medicine, There Was Individualized Inference

7/6/15                    2^nd International Workshop on Frontiers of Statistics with Applications to Finance, Fudan University,

Shanghai. I got more data, my model is more refined, but my estimator is getting worse! Am I just dumb?

7/27/15                  60^th World Statistics Congress (ISI), Rio, Brazil. Who is crazier: Bayes or Fisher? A Missing (Data)

Perspective on Fiducial Inference.

7/28/15                  60^th World Statistics Congress (ISI), Rio, Brazil. Computationally Efficient and Statistically Valid Detection

of Unspecified Structure in Astrophysical Images.

8/11/15                  2015 JSM, Seattle. There is Individualized Treatment. Why Not Individualized Inference?

1/6/16                    2016 Joint Mathematical Meetings (JMM), Seattle. The Potential and Perils of Preprocessing: Building New

Foundations.

3/7/16                    2016 ENAR Spring Meeting, Austin, Texas. Dissecting Multiple Imputation from a Multi-phase Inference

Perspective: What Happens When God's, Imputer's and Analyst's Models Are Uncongenial?

6/18/16                2016 ISBA Conference, Cagliari, Italy.  Calibration with Multiplicative Signals but Additive Errors:

A Bayesian Log Normal Approach

6/21/16                 4^th ICISE, Palermo, Italy.   Multi-Resolution Inference: An Engineering (Engineered?) Foundation of

Statistical Inference.

7/31/16                 2016 JSM, Chicago.   Discussion: Some Nobel-Prize (NP) Worthy i.i.d Ideas in Statistics.

8/2/16                   2016 JSM, Chicago.   Fiducial Inference: Fisher's Big Blunder or Big Bang?

8/18/16                 12^th International Conference on MCQMC (Monte Carlo and Quasi Monte Carlo) Methods in Scientific

Computing, Stanford University. Warp Bridge Sampling: The Next Generation.

9/6/16                   Royal Statistical Society 2016 Conference, Manchester, England.  How risky is our intuition for risk?
                                (Or: A quick, but not too dirty, Bayes formula for physicians, poets, and everyone in between… )

12/18/16               The First Eastern Asia Meeting on Bayesian Statistics (A Satellite Meeting of the 10th ICSA

                              International Conference), Shanghai Jiao Tong University. Extending the Concept of Prior Sample Sizes:

Assessing Prior Informativeness and Prior-Likelihood Discordance.

12/20/16                10th ICSA International Conference, Shanghai Jiao Tong University.  Fiducial Inference: Fisher’s Big

                             Blunder or Big Bang?

7/30/17                2017 JSM, Baltimore.  BFF Inferences with R’s: Replications, Relevance and Robustness.

8/1/17                  2017 JSM.  Baltimore.  Late Breaking Session:  From Euler to Clinton: An Unexpected Statistical

                            Journey.

8/2/17                  2017 JSM, Baltimore.  Discussion: Personalized Treatment: Sounds heavenly, but where on Earth did they

find my guinea pig?

6/4/18                    Conference on Statistical Learning and Data Science/Nonparametric Statistics,  Columbia University. Conducting Highly Principled Data Science: A Statistician's Job and Joy

7/30/18                2018 JSM, Vancouver. Annals of Applied Statistics Lecture: Statistical Paradises and Paradoxes in Big Data

(I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election

8/1/18                    2018 JSM, Vancouver.  Invited Panel:  From JSM 2017 To JSM 2018: My Painful N-of-1 Trial (Or: A Quest for Actionable Probability)

8/1/18                    2018 JSM, Vancouver. Discussion:  There is no free lunch, but ...

8/6/18                  2018 New York Scientific Data Summit, Brookhaven National Laboratory, Upton, NY. (Virtual Presentation)

 How Small Are Our Big Data: Turning the 2016 Surprise into a 2020 Vision

5/17/19                 2019 New England Stat Symposium.  Hartford, CT.  Conducting Highly Principled Data Science: A Statistician’s Job and Joy.

6/1/19                    2019 Symposiums on Data Science and Statistics.  Bellevue, Washington  Likelihood-Free EM: Self-Consistency for Incomplete or Irregular-Pattern Data

8/6/20                    2020 JSM, Virtual.  Discussion:  Data Privacy:  A Genuine Data Science Challenge.

8/6/20                    2020 JSM, Virtual.  Discussion:  Simultaneous Interpreting is Hard --- Lost (or Gain) in Translation

10/5/20                  JOIM (Journal of Investing Management) Virtual Conference on Applications of Data Science in Investment Management & COVID-19 Investment Implications.  Panel Discussion II:  Epidemiology of the Pandemic.

6/24/21                  Value of Science: Data, Products & Use Conference, National Center for Science and Engineering Statistics and Coleridge Initiative. Closing Comments:  What Are the Values of Data, Data Science, and Data Scientists?

8/8/21                    2021 JSM, Virtual.  A Multi-Resolution Theory for Approximating Infinite-P-Zero-N: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Trade-Off

8/10/21                  2021 JSM, Virtual.  Discussion: Is this really a BFF session???

11/15/21                2021 ISBA-EAC (East Asia Chapter) Meeting, Mirror Site to celebrate Jim Berger’s 70th Birthday, Atlantic City, NJ (Virtual) Statistical Learning with Low-resolution Information: There is No Free Lunch

5/25/22                  2022 New England Stat Symposium, U of Connecticut.  Double Your Variance, Dirtify Your Bayes, Devour Your Pufferfish, and Draw Your Kidstogram

6/28/22                  IMS Annual Conference, London (Virtual).  Discussion: My Shallow and Parallel Learning of E, F, and EF, and All Things BFF

8/7/22                    2022 JSM, Washington, DC.  Discussion: From “The Future of Data Analysis” to the Future of Data Science (and where statisticians can and should lead)

9/22/22                  Statistical methods and models for complex data -- 800 years of research to understand a complex world, University of Padua (Virtual). A Multi-resolution Theory for Approximating Infinite-p-Zero-n: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Tradeoff

5/3/23                  8^th Bayesian, Fiducial and Frequentist Conference (BFF8), U of Cincinnati. A Surprising U (and Surprised

You?): Finally Cauchy has its Moments

6/5/2023               36 New England Stat Symposium, Boston University. Discussant (of session on A Private Refreshment on

Statistical Principles and Senses.  Reimage and Reimagine Statistics:   A Theme on Variations 

8/7/2023               Special Invited Session on the Future of Data Science, JSM 2023.  Multi-resolution Meandering:

Personalized Treatments, Individual Privacy, Machine Unlearning, and a World without Randomness

8/7/2023               Invited Panel on Statistical Privacy in the 21st Century: Census and Consensus, JSM 2023. What does

Differential Privacy (DP) guarantee?
 

 

Invited Workshop Presentations and Lectures, Short Courses, and Tutorials:

2/19/93                  Workshop on “Model Selection, Bayes Factor, and Sensitivity Study”, UCLA.  Coherent Multiple-Imputation Inference Under Incoherent Models

7/12-13/94            Summer Workshop on “Computational and Applied Statistics”, Institute of Applied Mathematics, Chinese Academy of Science, Beijing.  Lectures on Recent Development of EM-type Algorithms

5/3/96                    Spring Conference on “Simulation in the 90s”, ASA Chicago Chapter.  An Overview of Algorithms for Simulating from Complex Distributions

1/15/98                  Workshop on “Novel Monte Carlo algorithms and Their Applications”, Center for Nonlinear Studies, Los Alamos National Laboratory.  Monte Carlo Methods: A Statistician’s View and Some Recent Work

5/18/98                  International Symposium for Celebrating 10^th Anniversary of the MSc. in Biostatistics Program, Limburgs Universitair Centrum, Diepenbeek, Belgium.  Sensitivity under Uncongeniality: A Hybrid Framework with Illustration

7/16-20/98            Workshop on “Statistical Methods for Incomplete Data and High-dimensional Data”, Stats and OR, Fudan University, Shanghai.  Lectures on Recent Developments in Data Augmentation Techniques and Computation of Normalizing Constants

10/25/98               Workshop on “Monte Carlo Methods”, Fields Institute for Research in Mathematical Science, Toronto.  The Art of Data Augmentation and Multi-stage Backward Coupling

11/1-12/31/00      Math., Hong Kong U. of Science and Technology.   Lectures on Statistical Algorithms

9/31/02                  The First Workshop on Monte Carlo Methods, Cape Cod, MA.  Inference with Monte Carlo Data: The Problem of “Knowing Too Much”

8/9/03                    International Workshop on Bayesian Data Analysis, UC Santa Cruz.  A Bayesian Framework for Quantifying Incomplete Information In Statistical and Genetic Hypothesis Testing.

8/27/04                  The Second Workshop on Monte Carlo Methods, Cambridge, MA.  Warp Bridge Sampling for Likelihood and Bayesian Computation

9/10/04                  Astrostatistics Workshop: Data Analysis Challenges in Astrophysics, High-Energy Astrophysics Division (HEAD) Meeting, New Orleans, LA.   An Introductory Overview of Statistical Methods for Discrete Time Series

1/12/05                  MCMSki: The Past, Present, and Future of Gibbs Sampling, Bormio, Italy.  Going Beyond Compatibility: The Future of the Gibbs Sampler?

2/1/05                    Workshop on “Markov Chains in Algorithms and Statistical Physics”, Mathematical Sciences Research Institute, Berkeley, CA.  Computing Normalizing Constants: A Bridge Between Statistical Physics and Statistical Computing

6/11/06                  Graybill Conference 2006: Multiscale Methods and Statistics – A Productive Marriage. A Crash Course in Wavelets Methods (Joint taught with Thomas Lee and Patrick Wolfe).

7/16/06                  Statistical inference Problems in High Energy Physics and Astronomy, Banff, Canada.  Dealing with Nuisances: Principled and Ad Hoc Methods

5/13/07                  Third Workshop on Monte Carlo Methods, Cambridge, MA.  Statistical Physics and Statistical Computing:  A Critical Link.

6/4/07                    DIMACS Workshop on Markov Chain Monte Carlo: Synthesizing Theory and Practice, Rutgers U.  Tutorial: Markov Chain Monte Carlo: Practice – The Full Monte Carlo: A Live Performance (with Stars), Part I and Part II.

11/16/07               Stochastics and Dependence in Finance, Risk Management, and Insurance, Radcliffe Exploratory Seminar.  Espousing Modern Computation with Classical Statistics: Sufficiency, Ancillarity and a New Generation of MCMC.

12/1/07                  Workshop on Monte Carlo Methods, Harvard U.  Banquet Speaker:  The Full Monte Carlo: A Live Performance

3/20/09                  Workshop on Markov chain Monte Carlo and Related Methods, University of Warwick, UK.  To Center or Not to Center: That is Not the Question: An Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Efficiency

9/29/09                  Statistical Frontiers of Astrophysics, Institute of Physics and Mathematics of Universe, U of Tokyo, Japan.  The Full Monte Carlo: A Live Performance with Stars.

1/18/11                  GSAS J-term Short Course, Harvard. Trivial Mathematics but Deep Statistics: Simpson’s Paradox and Its Impact on Your Life.

3/29/11                  THURJ (The Harvard Undergraduate Research Journal) invited tutorial (jointly with J. Blitzstein).  The Statistical Magic of Markov chain Monte Carlo.

6/1/11                    TigerFest, Chapel Hill High School, Chapel Hill, NC.  Trivial Mathematics but Deep Statistics: Simpson’s Paradox and Its Impact on Your Life.

6/2/11                    Career Center, Winston-Salem/Forsyth County Schools, NC.  Trivial Mathematics but Deep Statistics: Simpson’s Paradox and Its Impact on Your Life.

7/19/11                  BLISS/PRIMO/PRISE program for undergraduate research, Harvard. Trivial Mathematics but Deep Statistics: Simpson’s Paradox and Its Impact on Your Life.

8/5/11                    GSAS/ELP (English Language Program), Harvard.  Trivial Mathematics but Deep Statistics: Simpson’s Paradox and Its Impact on Your Life.

9/24/11                  Workshop on Computational Methods in Applied Sciences, Columbia U. Let's Practice What We Preach: Likelihood Methods for Monte Carlo Data

8/21/12                  ICIC (Imperial Centre for Inference and Cosmology) Inaugural Workshop, Imperial College.  I Got More Data, My Model is More Refined, But My Estimator is Getting Worse! Am I Just Dumb?

11/30/12               Workshop on Performance Analysis of Monte Carlo Methods, ICERM, Brown U. The Best Performing Monte Carlo Estimator?  A Likelihood Framework for Compromising between Statistical Efficiency and Computational Efficiency

12/19-20/12         Short Course on Learning to Be a Better Statistician: Think Deeper and Compute Faster, Institute of Statistical Science, Academia Sinica.  Day One: Statistical Insight and Intuition.  Day Two: Basic and Advanced Markov Chain Monte Carlo (MCMC)

1/19/13                  Workshop on New Directions in Monte Carlo Methods, University of Florida.  Warp Bridge Sampling: The Next Generation.

6/20/13                  BLISS/PRIMO/PRISE - Program for Undergraduate Research, Harvard.  The Full Monte Carlo: A Live Performance.

5/8/14                  Abel Symposium on High Dimensional Analysis, Lofoten, Norway.  The Potential and Perils of Preprocessing: Exploring distributed sufficiency.

10/1/14                  The Paradise and Paradox of Statistics.  Interactive lectures for China Entrepreneur Club, Harvard Business School.

11/11/14                1^st Workshop on BFF inference and Statistical Foundations, East China Normal University. There’s Personalized Medicine. Why Not Personalized Inference?

11/15/14               Chin Long Chiang's Memorial Workshop, University of California at Berkeley. Biostatistics vs. Biostatistical Science: A Statistical Verdict.

7/ 5/15                   2^nd Workshop on BFF Inference and Statistical Foundations, East China Normal University. Who is Crazier: Bayes or Fisher? A Missing (Data) Perspective on Fiducial Inference.

7/23/15                  Big Data Senior Steering Group (BDSSG) meeting, NSF, Washington, DC. Statistical Paradises and Paradoxes in Big Data.

6/24/16                 I-Like Workshop 2016, Lancaster University. Warp Bridge Sampling: The Next Generation.

9/20/16                 Workshop on Statistical Issues in Experimental Neutrino Physics, Fermilab, Batavia, IL. Fiducial Inference:

                                                Fisher's Big Blunder or Big Bang?

10/1/16                 1^st Workshop on Higher-Order Asymptotics and Post-Selection Inference, University of Washington, St.

                                Louis. Building a Statistical Theory for Individualized Treatments: A Multi-resolution Perspective.

4/4/17                   Webnar for Prevention Science and Methodology Group (PSMG), Building a Statistical Theory for

                             Individualized Treatments: A Multi-resolution Perspective.

8/12/17                2nd Workshop on Higher-Order Asymptotics and Post-Selection Inference, University of Washington, St.

                                Louis.  Struggling with large p and small n?  How about (potentially) infinite p and (essentially) zero n?

10/12/17               Closing Plenary Panel on “The Radical Prescription for Change”, ASA Symposium on Statistical Inference.  

                                Double Your Variance, Dirtify Your Bayes, & Devour Your Pufferfish

11/28/17               Harvard Data Science Initiative (HDSI) 45/45 Lecture Series.  The Law of Large Populations:

                                The return of the long-ignored N and how it can affect our 2020 vision.

8/12/18                3rd Workshop on Higher-Order Asymptotics and Post-Selection Inference, University of Washington, St.

                                Louis.  Was there ever a pre-selection inference?

10/25/18              2018 Harvard QuantBio Retreat, Endicott College, Beverly, MA. Handling (Potentially) Infinite p and

                                (Essentially) Zero n: A Multi-Resolution Framework For Individualized Learning.

3/20/19                  CRiSM Day on Bayesian Intelligence, University of Warwick, Artificial Bayesian Monte Carlo Integration:

                                A Practical Resolution to the Bayesian (Normalizing Constant) Paradox.

8/19/19                4^th Workshop on Higher-Order Asymptotics and Post-Selection Inference, University of Washington, St.

                                Louis.  The conditionality principle is (still) safe and sound, but our large-p-small-n models are ill (defined)

8/30/19                Workshop on “Foundations of Computational Sciences”, Center for Mathematical Sciences and Its

Applications, Harvard University.  Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox

9/24/19                 Symposium on the Reproducibility and Replicability in Science, Notational Academies of Sciences, DC. 

                             Moderator, invited panel on “Replicability: What It Means to Replicate a Study?”

10/29/20              Virtual Workshop on Dealing with COVID-19 in Theory and Practice, Institute of Mathematical and

                                Statistical Innovation (IMSI), The University of Chicago. From COVID-19 Testing to Election Prediction:

                                How Small Are Our Big Data?

2/25/21                 Workshop on Emerging Areas of Science, Engineering, and Medicine for the Courts, National Academies of

                                Sciences, Engineering, and Medicine (Virtual) The biggest enemy to reliable evidence from Data Science:    Selection Bias (intentional or unintentional)

5/18/21                 Workshop on Decision Making in Health and Medical Care: Modeling and Optimization (Institute for

                                Mathematical and Statistical Innovation, Chicago, Virtual) A Stressful Tour of the COVID-19 Issue of the

                                Harvard Data Science Review.

10/15/21              2nd Annual Workshop, Emerging Data Science Methods for Complex Biomedical and Cyber Data, Augusta

                                University (Virtual). Personalized Treatments: Sounds Heavenly, but Where Did They Find My Guinea Pigs?

11/3/21                PHYSTAT-Systematics Remote Workshop, CERN. Partially Bayes: An Inter-Perspective Method for

                                Handling Nuisance Parameters

7/5/22                  Summer School on Modern Methods in Survey Sampling，University of Ottawa.  Data Minding.

8/12/22                Workshop on Statistical Foundations and Applications of Machine Learning and Artificial Intelligence:

                                A conference to honor Shaw-Hwa Lo’s 70th Birthday and His Collaboration with Herman Chernoff.

                                Miniaturizing Data Defect Correlation: A Versatile Strategy for Handling Non-Probability Samples

3/17/23                 3rd Annual Workshop, Emerging Data Science Methods for Complex Biomedical and

Cyber Data, Augusta University (Virtual). Privacy, Data Privacy, and Differential Privacy.

5/4/23                   NBER Workshop on Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches

                                and Their Consequences, Cambridge, MA.  Discussion:  Multi-phases Inferences, Uncongeniality, and

                                Multiple Imputation

12/10/23               2023 Australia Statistics Conference, Short Course:  Deep Statistics for More Rigorous and Efficient Data

                                Science.

12/13/23               2023 AI in Society Workshop - Celebrating HMI, Introducing Integrated AI, Australia National University.

                                Privacy, Data Privacy, and Differential Privacy (Jointly with James Bailie)