Online Program Home
My Program

Abstract Details

Activity Number: 347 - Computationally Intensive Bayesian Methodology
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #305030 Presentation
Title: Bayesian Inference for the Common Location Parameter of Several Shifted-Exponential Populations
Author(s): Sumith Gunasekera*
Companies: The University of Tennessee - Chattanooga
Keywords: Bayesian hypothesis testing; Common location parameter; Credible intervals; Gibbs sampling; Shifted-exponential distribution.

Consider k random samples which are independently drawn from k shifted-exponential distribution, with different scale parameters and a common location parameter. On the basis of the given samples and in a Bayesian framework, we address the problem of point and interval estimation of the location parameter under the conjugate priors. Moreover, we also address the problem of testing the equality of the location parameters. We propose Bayesian hypothesis testing procedures for the equality of the location parameters under the noninformative prior. So we propose the default Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Our proposed Bayesian procedures are compared and contrasted, via a comparison study, a simulation study, and a real-world data analysis, to the existing exact classical procedures and the generalized variable procedures.

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

Back to the full JSM 2019 program