Bayesian Model Selection and Hypothesis Tests

Ming-Hui Chen (), Dipak K. Dey (), Peter Müller (), Dongchu Sun () and Keying Ye ()
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Ming-Hui Chen: University of Connecticut, Department of Statistics
Dipak K. Dey: University of Connecticut, Department of Statistics
Peter Müller: The University of Texas, M. D. Anderson Cancer Center, Department of Biostatistics
Dongchu Sun: University of Missouri-Columbia, Department of Statistics
Keying Ye: University of Texas at San Antonio, Department of Management Science and Statistics, College of Business

Chapter Chapter 4 in Frontiers of Statistical Decision Making and Bayesian Analysis, 2010, pp 113-155 from Springer

Abstract: Abstract Model comparison remains an active research frontier in Bayesian analysis. The chapter introduces related specific research problems, including the selection of a number of components in a mixture model and the choice of a training sample size when using virtual simulated training samples. The chapter also discusses an intriguing general property that sets Bayesian testing apart from frequentist testing, by effectively rewarding honest choice of an alternative hypothesis. Cheating does not pay.

Keywords: Mixture Model; Training Sample; Bayesian Information Criterion; Gaussian Mixture Model; Bayesian Model Average (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-1-4419-6944-6_4

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