Colloquia

2017 Jan 30

Colloq: Alexander (Sasha) Rakhlin

4:15pm to 5:15pm

Location: 

Science Center Hall E

An Optimal Aggregation Procedure For Nonparametric Regression

How can one combine a collection of estimators of a regression function into a good aggregate? In the last 15 years, this age-old question has received increasing attention within the Mathematical Statistics community. A closely related question of regression in misspecified models has been studied within Statistical Learning using the techniques of empirical processes. We outline the...

Read more about Colloq: Alexander (Sasha) Rakhlin
2014 Feb 14

StatColloq: Tracy Ke

4:15pm to 5:15pm

Location: 

Science Center Rm. 705
Covariance Assisted Screening and Estimation
2014 May 05

Stat Colloq: Joseph Kelly

2:00pm to 3:30pm

Location: 

Science Center Rm. 705
Advances in the Normal-Normal Hierarchical Model
2015 Apr 13

Stat Colloq: Hugh Chipman

4:15pm to 5:15pm

Location: 

Science Center Rm. 705
A Statistical Pocket Knife: Generalizing and Extending Ensemble Models
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
2013 Dec 05

Stat Colloq: Valeria Espinosa

2:30pm to 3:30pm

Location: 

Science Center Rm. 705
A Bayesian Perspective on Unreplicated Factorial Experiments Using Potential Outcomes
2014 Mar 31

ApplMathColloq: Noureddine El Karoui

3:00pm to 4:00pm

Location: 

Maxwell Dworkin G125
Random matrices and high-dimensional M-estimation: applications to robust regression, penalized robust regression and GLMs I will discuss the behavior of widely used statistical methods in the high-dimensional setting where the number of observations, n, and the number of predictors, p, are both large. I will present limit theorems about the behavior of the corresponding estimators, their asymptotic risks etc... The results apply not only to robust regression estimators, but also Lasso-type estimators and many much more complicated problems. Some of the results answer a question raised by... Read more about ApplMathColloq: Noureddine El Karoui
2014 Nov 03

Stat Colloq: Ben Calderhead

4:15pm to 5:15pm

Location: 

Science Center Rm. 705
A General Construction for Parallelising Metropolis-Hastings Algorithms
2015 Oct 19

Stat Colloq: Art B. Owen

4:15pm to 5:15pm

Location: 

Science Center 300H
Moment Based Methods for Large Crossed Unbalanced Random Effects Models

Pages