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The Bayes Factor Versus Other Model Selection Criteria for the Selection of Constrained Models

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Bayesian Evaluation of Informative Hypotheses

Part of the book series: Statistics for Social and Behavioral Sciences ((SSBS))

Abstract

Model assessment and model comparison are a crucial part of statistical analysis. Due to recent computational advances, sophisticated techniques for Bayesian model assessment are becoming increasingly popular. There is a rich literature on Bayesian methods for model assessment and model comparison, including [1, 3, 6, 9, 10, 13, 14, 16, 17, 18, 24, 26, 28, 30, 32, 33, 34, 36]. The scope of Bayesian model assessment can be investigated via model diagnostics, goodness of fit measures, or posterior model probabilities (or Bayes factors). A comprehensive account of model diagnostics and related methods for model assessment is given in [15].

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Author information

Authors and Affiliations

  1. Department of Statistics, University of Connecticut, 215 Glenbrook Road, U-4120, Storrs, CT, 06269, USA

    Ming-Hui Chen

  2. Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, NIH, Rockville, MD, 20852, USA

    Sungduk Kim

Authors
  1. Ming-Hui Chen
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  2. Sungduk Kim
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Correspondence to Ming-Hui Chen .

Editor information

Editors and Affiliations

  1. Department of Methods and Statistics, Faculty of Social Sciences, University of Utrecht, P.O. Box 80140, 3508 TC, Utrecht, The Netherlands

    Herbert Hoijtink  & Irene Klugkist  & 

  2. Department of Clinical and Health Psychology, Faculty of Social Sciences, University of Utrecht, P.O. Box 80140, 3508 TC, Utrecht, The Netherlands

    Paul A. Boelen

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Chen, MH., Kim, S. (2008). The Bayes Factor Versus Other Model Selection Criteria for the Selection of Constrained Models. In: Hoijtink, H., Klugkist, I., Boelen, P.A. (eds) Bayesian Evaluation of Informative Hypotheses. Statistics for Social and Behavioral Sciences. Springer, New York, NY. https://doi.org/10.1007/978-0-387-09612-4_8

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