Previous Wellner Lectures
The Spring 2022 Jon A. Wellner Lecture
Tuesday, March 29, 2022
3:00 p.m. – Lecture in TLC 031
4:00 p.m. – Refreshments in the IRIC Atrium
Fitting stochastic epidemic models to noisy surveillance data: are we there yet?
Vladimir N. Minin, Ph.D.
Professor, Department of Statistics and Associate Director of the Infectious Disease Science Initiative, University of California, Irvine
Stochastic epidemic models describe how infectious diseases spread through a population of interest. These models are constructed by first assigning individuals to compartments (e.g., susceptible, infectious, and recovered) and then defining a stochastic process that governs the evolution of sizes of these compartments through time. I will review multiple lines of attack of a challenging and not fully solved problem of fitting these models to noisy infectious disease surveillance data. These solutions involve a range of mathematical techniques: particle filter Markov chain Monte Carlo algorithms, approximations of stochastic differential equations, and Poisson random measure-based Bayesian data augmentation. Importantly, many of these computational strategies open the door for integration of multiple infectious disease surveillance data streams, including less conventional ones (e.g., pathogen wastewater monitoring and genomic surveillance). Such data integration is critical for making key parameters of stochastic epidemic models identifiable. I will illustrate the state-of-the-art statistical inference for stochastic epidemic models using Influenza, Ebola, and SARS-CoV-2 surveillance data and will conclude with open problems and challenges that remain to be addressed.
About the Speaker
Minin’s research interests revolve around developing statistically rigorous solutions to problems that arise in biological sciences. These solutions often involve formulating stochastic models that can describe complex dynamics of biological systems and devising computationally efficient algorithms to fit these models to data. Minin is currently most active in infectious disease epidemiology, working on Bayesian estimation of disease transmission model parameters. His other research interests include phylogenetics, population genetics, computational immunology, and systems biology. Minin received a B.S. in Mathematics from Odesa National University, an M.S. in Mathematics from the University of Idaho, and a Ph.D. in Biomathematics from the University of California, Los Angeles.
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
Nonparametric Inference Under Shape Constraints: Past, Present and Future
Richard J. Samworth, Ph.D.
Professor of Statistical Science and Director of the Statistical Laboratory, University of Cambridge
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.
- 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
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
New Multiplier Inequalities and Applications
Jon A. Wellner, Ph.D.
Professor of Statistics and Biostatistics, University of Washington
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.]