Previous Wellner Lectures
The 2019 Jon A. Wellner Lecture
Tuesday, September 24, 2019
3:00 p.m. – Refreshments in the Paul J. Joyce Faculty-Staff Lounge, Brink Hall
4:00 p.m. – Lecture in TLC 031
Title
Nonparametric Inference Under Shape Constraints: Past, Present and Future
Speaker
Richard J. Samworth, Ph.D.
Professor of Statistical Science and Director of the Statistical Laboratory, University of Cambridge
Abstract
Traditionally, we think of statistical methods as being divided into parametric approaches, which can be restrictive, but where estimation is typically straightforward (e.g. using maximum likelihood), and nonparametric methods, which are more flexible but often require careful choices of tuning parameters. The area of nonparametric inference under shape constraints sits somewhere in the middle, seeking in some ways the best of both worlds. I will give an introduction to this currently very active area, providing some history, recent developments and a future outlook.
About the Speaker
Professor Richard J. Samworth's main research interests are in nonparametric and high-dimensional statistics. Particular topics include shape-constrained estimation problems; data perturbation methods (e.g. subsampling, bootstrap sampling, random projections, knockoffs); nonparametric classification; (conditional) independence testing; estimation of entropy and other functionals; changepoint detection and estimation; missing data; variable selection; and applications, including genetics, archaeology and oceanography.
Awards
- Fellow of the American Statistical Association
- Fellow of the Institute of Mathematical Statistics
- The Royal Statistical Society?s Research Prize in 2008
- The Guy Medal in Bronze, Royal Statistical Society, in 2012
- The COPSS Presidents' Award 2018
- IMS Medallion Lecture 2018
- The Adams Prize 2017
- Philip Leverhulme Prize 2014
Previous Lectures
University of Idaho Wellner Lecture
Nonparametric Inference Under Shape Constraints: Past, Present and Future by Richard Samworth
The 2018 Jon A. Wellner Lecture
Thursday, September 6, 2018
3:00 p.m. – Refreshments in Paul Joyce Faculty-Staff Lounge, Brink Hall
4:00 p.m. – Lecture in TLC 031
Title
New Multiplier Inequalities and Applications
Speaker
Jon A. Wellner, Ph.D.
Professor of Statistics and Biostatistics, University of Washington
Abstract
Multiplier inequalities have proved to be one of the key tools of modern empirical process theory, with applications to central limit theorems, bootstrap theory, and weighted likelihood methods in statistics. In this talk I will review some classical multiplier inequalities, present a new multiplier inequality, and discuss several statistical applications. The applications include new results concerning convergence rates of least squares estimators (LSE) in regression models with possibly heavy-tailed errors. Particular cases involving sparse linear regression and shape restrictions will be mentioned.
[This talk is based on the University of Washington Ph.D. work of Qiyang (Roy) Han.]