The Statistics Seminar Speaker for November 16, 2016 is Eitan Greenshtein, who is currently with the Israel Bureau of Statistics. He received his PhD from Cornell University in 1990, and has served as a faculty member at Ben-Gurion University, Technion, and Haifa University. He has also been a visiting faculty at Purdue, Wharton, Duke and (presently) Rutgers.
Title: Non-parametric empirical Bayes improvement of common shrinkage estimators
Abstract: We consider the problem of estimating a vector (µ1,...,µn) of normal means under a squared loss, based on independent Y_i ∼ N(µ_i,1), i = 1,...,n. We use ideas and techniques from non-parametric empirical Bayes, to obtain asymptotical risk improvement of classical shrinkage estimators, such as, Stein's estimator, Fay-Herriot, Kalman filter, and more. We consider both the sequential and retrospective estimation problems. We elaborate on state-space models and the Kalman filter estimators. The performance of our improving method is demonstrated both through simulations and real data examples.
Joint work with Ariel Mansura, and Ya'acov Ritov