Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 355 - Advanced Bayesian Topics (Part 4)
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #319070
Title: Bayesian Variance Estimation and Hypothesis Testing Using Inference Loss Functions
Author(s): Kendrick Li* and Ken Rice
Companies: Department of Biostatistics, University of Washington and Department of Biostatistics, University of Washington
Keywords: Decision theory; Robust standard error

Many frequentist parametric statistical methods have large sample Bayesian analogs. However, there is no general Bayesian analog of "robust" covariance estimates, that are widely-used in frequentist work. We propose such an analog, produced as the Bayes rule under a form of balanced loss function, that combines elements of standard parametric inference with fidelity of the data to the model. Besides being the large-sample equivalent of its frequentist counterpart, we show by simulation that the Bayesian robust standard error can also be used to construct Wald confidence intervals that improve small-sample coverage.

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

Back to the full JSM 2021 program