Statistics Seminar Series

Semester Schedule: Statistics - Spring 2013

Seminars are on Mondays
Time:12:00 - 1:00 PM Location: Room 903, 1255 Amsterdam Avenue, Tea and Coffee will be served before the seminar at 11:30 AM, Room 1025

Jan 28

Bin Yu
Departments of Statistics and EECS, University of California at Berkeley
www.stat.berkeley.edu/~binyu

Abstract
Reproducibility is imperative for any scientific discovery. More often than not, modern scientific findings rely on statistical analysis of high-dimensional data. At a minimum, reproducibility manifests itself in stability of statistical results relative to “reasonable” perturbations to data and to the model used. Jacknife, bootstrap, and cross-validation are based on perturbations to data, while robust statistics methods deal with perturbations to models.

In this talk, a case is made for the importance of stability in statistics. Firstly, we motivate the necessity of stability of interpretable encoding models for movie reconstruction from brain fMRI signals. Secondly, we find strong evidence in the literature to demonstrate the central role of stability in statis- tical inference. Thirdly, a smoothing parameter selector based on estimation stability (ES), ES-CV, is proposed for Lasso, in order to bring stability to bear on cross-validation (CV). ES-CV is then utilized in the encoding models to reduce the number of predictors by 60% with almost no loss (1.3%) of prediction performane across over 2,000 voxels. Last, a novel “stability” argument is seen to drive new results that shed light on the intriquing interactions between sample to sample varibility and heavier tail error distribution (e.g. double-exponential) in high dimensional regression models with p predictors and n independent samples. In particular, when p/n → κ ∈ (0.3, 1) and error is double-exponential, OLS is a better estimator than LAD.

Bio:
Bin Yu is Chancellor's Professor in the Departments of Statistics and Electrical Engineering & Computer Science at UC Berkeley.

She was Chair of Statistics Department at UC Berkeley from 2009 to 2012. She has published over 100 scientific papers in premier journals and conferences in Statistics, EECS, remote sensing and neuroscience. These papers cover a wide range of research topics, including empirical process theory, information theory (MDL), MCMC methods, signal processing, machine learning, high dimensional data inference (boosting and Lasso and sparse modeling in general), and interdisciplinary data problems.  She has served on many editorial boards for journals such as Annals of Statistics, Journal of American Statistical Association, and Journal of Machine Learning Research.

She was a 2006 Guggenheim Fellow, co-recipient of the Best Paper Award of IEEE Signal Processing Society in 2006, and the 2012 Tukey Memorial Lecturer of the Bernoulli Society (selected every four years).
She is a Fellow of AAAS, IEEE, IMS (Institute of Mathematical Statistics) and ASA (American Statistical Association).

She is currently President-Elect of IMS (Institute of Mathematical Statistics).  She is serving on the Scientific Advisory Board of IPAM (Institute for Pure and Applied Mathematics) and on the Board of Mathematical Sciences and Applications of NAS. She was co-chair of the National Scientific Committee of SAMSI (Statistical and Applied Mathematical
Sciences Institute), and was on the Board of Governors of IEEE-IT Society.

Feb 4

Tommy Wright, US Census Bureau

Feb 18

Joseph Pickrell, Harvard Medical School

Feb 25

Yimin Xiao, Michigan State University

March 11

Ethan Anderes, UC Davis

Peter Hoff, University of Washington

*Friday, March 29

Runze Li, Penn State University

Larry Carin, Duke University

Peter Hall, University of Melbourne

Lester Mackey, Stanford University

Thomas Mikosch, University of Copenhagen

Tingting Zhang, University of Virginia