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Activity Number: 260 - SPEED: Topics in Bayesian Analysis
Type: Contributed
Date/Time: Monday, July 30, 2018 : 3:05 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #332665
Title: A Bayesian Model Selection Approach to Multiple Comparisons
Author(s): Javier E. Flores* and Andrew Neath and Joseph Cavanaugh
Companies: University of Iowa and SIU Edwardsville and University of Iowa
Keywords: Multiple Comparisons; Model Selection; BIC

Consider the setting in which independent samples from several populations are taken for the purpose of between-group comparisons. Specifically, interest lies in the determination of clusters where mean levels are equal. To this end, multiple comparisons testing procedures are often employed. We formulate the hypothesis testing problem of determining equal-mean clusters as a Bayesian model selection problem. A comprehensive collection of models is formulated by specifying mean structures that represent all possible combinations of equal mean levels. The Bayesian information criterion is then used to approximate posterior model probabilities and posterior probabilities associated with pairwise mean equalities. Information from all competing models is combined through Bayesian model averaging in an effort to provide a more realistic accounting of uncertainty. An example illustrates how the Bayesian approach leads to a logically sound presentation of multiple comparison results.

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

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