Bayesian multiple comparisons and model selection. (17th November 2017)

• Record Type: Journal Article
• Authors: Neath, Andrew A.; Flores, Javier E.; Cavanaugh, Joseph E.
• Is Part Of: Wiley interdisciplinary reviews. Volume 10:Number 2(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-11-17
• Subjects: analysis of variance -- Bayesian information criterion -- family wise error rate -- hierarchical modeling -- multiplicity adjustment -- prior specification -- shrinkage estimation; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1420
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract : The testing of multiple hypotheses is an important consideration in many statistical analyses. A theme for multiple comparisons problems under a frequentist paradigm is the need for an adjustment to control the overall error probability for the false detection of null effects. Our review will focus on Bayesian approaches to multiple comparisons problems. Under a Bayesian paradigm, multiplicity adjustments arise from a concern that many of the effects to be tested are null. We will discuss how Bayesian models provide a multiplicity adjustment through a prior placing increased probability on null effects, or through hierarchical modeling. We will also show how the Bayesian information criterion for model selection fits naturally into the study of multiple comparisons problems. WIREs Comput Stat 2018, 10:e1420. doi: 10.1002/wics.1420 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms Data: Types and Structure > Traditional Statistical Data Abstract : Estimated mean differences in the log‐transformed creatine kinase levels. (1) corresponds to empirical group means; (2) corresponds to estimated group means under Bayesian model averaging; (3) corresponds to estimated group means under the highest posterior probability model.