Large-scale Inference and the NEST Estimator

October 2, 2019
3:00 to 4pm

Luella Fu, PhD, Assistant Professor, San Francisco State University

Large-scale inference covers a set of statistical methodologies that address problems of detecting and estimating significant data amongst thousands or more points.  We start with an overview of four key questions for large-scale inference with examples from modern-day applications.  In the context of these questions, we focus on the problem of assessing school quality from standardized test scores.  Essentially, we want to estimate a vector of normal means with heteroscedastic variances.  We propose the "Nonparametric Empirical Bayes SURE Tweedie's" (NEST) estimator, which estimates the marginal density of the data using a smoothing kernel that weights observations according to their distance from other data based on both observed test scores and their standard deviation.  NEST then applies the estimated density to a generalized version of Tweedie's formula to estimate the corresponding mean vector.  Additionally, a Stein-type inbiased risk estimate (SURE) criterion is developed to select NEST's tuning parameters.  We discuss NEST in terms of shrinkage estimators, its algorithm, its theory, and its numeric performance.


Event Type: 
Biostatistics and Bioinformatics Seminar