Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 360 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #313452
Title: On Robust Pseudo-Bayes Estimation for the Independent Non-Homogeneous Set-Up
Author(s): Tuhin Majumder*
Companies: North Carolina State University
Keywords: Robustness; Bayes Estimator; Pseudo-Bayes; Linear Regression; Influence Funtcion; Breakdown Point
Abstract:

The ordinary Bayes estimator based on the posterior density suffers from the potential problems of non-robustness under data contamination or outliers. In this work, we consider the general set-up of independent but non-homogeneous (INH) observations and study a robustified pseudo-posterior based estimation for such parametric INH models. In particular, we focus on a particular robustified posterior, namely R-alpha posterior, developed by Ghosh and Basu (2016) for IID data and later extended by Ghosh and Basu (2017) for INH set-up, where its desirable properties have been illustrated. We have developed Bernstein-von Mises types asymptotic normality results and Laplace type asymptotic expansion of the robust posterior for INH set-up. The robustness of this R-alpha posterior and associated estimators are theoretically examined through influence function analyses. A high breakdown point result is derived for the expected R-alpha posterior estimators of the location parameter under a location-scale type model. Extensive simulations and real life data applications in fixed-design linear regression models numerically illustrate the robustness properties of the pseudo-Bayes estimators.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program