2016 - 2017

2016 Oct 24

Colloq: Tracy Ke

4:15pm to 5:15pm

Location: 

Science Center Rm. 300H

Social Networks for Statisticians

We have collected a data set for the social networks of statisticians. The data set consists of the meta information (e.g., authors, abstracts, citation counts) of about 70,000 papers in 36 representative journals in statistics and related fields, from 1984-2015. Our data collection project (which we may call it the Phase II) is a continuation of the recent data collection project by Ji and Jin (which we may call the Phase I)....

Read more about Colloq: Tracy Ke
2016 Nov 14

Colloq: Sarah Filippi

4:15pm to 5:15pm

Location: 

Science Center Rm. 300H

Bayesian nonparametric approaches to quantifying dependence between random variables

Nonparametric and nonlinear measures of statistical dependence between pairs of random variables have proved themselves important tools in modern data analysis, where the emergence of large data sets can support the relaxation of linearity assumptions implicit in traditional association scores such as correlation. In this talk, I will present two Bayesian nonparametric...

Read more about Colloq: Sarah Filippi
2016 Sep 12

Colloq: Xiaole Shirley Liu

4:15pm to 5:15pm

Location: 

Science Center Rm. 300H

Inference of Tumor Immunity and T-cell Receptor Repertoire from TCGA RNA-seq Data

We developed a computational approach to study tumor-infiltrating immune cells and their interactions with cancer cells. Analysis of over ten thousand RNA-seq samples from the Cancer Genome Atlas (TCGA) identified strong association between immune infiltrates and patient clinical features, viral infection status, and cancer genetic alterations. We found that melanomas with high...

Read more about Colloq: Xiaole Shirley Liu
2017 Feb 06

Colloq: Rob Tibshirani

4:15pm to 5:15pm

Location: 

Science Center Hall D

Recent Advances in Post-Selection Statistical Inference

We describe the problem of “post-selection inference.” This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have “cherry-picked”—searched for the strongest associations—means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large...

Read more about Colloq: Rob Tibshirani
2016 Sep 19

Colloq: Jingchen Liu

4:15pm to 5:15pm

Location: 

Science Center Rm. 300H
One of the main tasks of statistical models is to characterize the dependence structures of multi-dimensional distributions. Latent variable model takes advantage of the fact that the dependence of a high dimensional random vector is often induced by just a few latent (unobserved) factors. In this talk, we present several problems regarding latent variable models. When the dimension grows higher and the dependence structure becomes more complicated, it is hardly possible to find a low dimensional parametric latent variable model that fits well. We enrich the model by including a graphical... Read more about Colloq: Jingchen Liu
2016 Nov 15

STAT 310: Kai Zhang

1:15pm to 2:30pm

Location: 

Science Center Rm. 705

BET on Independence

We study the problem of model-free dependence detection. This problem can be difficult even when the marginal distributions are known. We explain this difficulty by showing the impossibility to uniformly consistently distinguish degeneracy from independence with any single test. To make model-free dependence detection a tractable problem, we introduce the concept of binary expansion statistics (BEStat) and propose the binary expansion testing (BET) framework. Through simple mathematics, we convert the dependence detection problem to a multiple testing...

Read more about STAT 310: Kai Zhang
2017 Feb 22

Special Colloq: Dominique Haughton

4:00pm to 5:00pm

Location: 

Science Center Hall E

Adventures in Music Analytics

Dominique Haughton, Bentley University, Université Paris 1 and Université Toulouse 1, with the Music Analytics Group at Bentley University

This talk will give an overview of projects at the frontier between data analysis and music (“music analytics”) and will present preliminary results of an analysis of the success of Kickstarter rock music projects, using features extracted from audio files with the Echonest/Spotify API (Application Program Interface). We also discuss current state of the art in defining measures of distance between...

Read more about Special Colloq: Dominique Haughton
2017 Feb 28

STAT 300: Lo-Hua Yuan

12:00pm to 1:00pm

Location: 

Science Center Rm. 705
Ways to identify principal causal effects in single- vs. multi-site trials

Pages