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Model Comparison, Model Checking, and Hypothesis Testing

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Applied Bayesian Statistics

Part of the book series: Springer Texts in Statistics ((STS,volume 98))

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Abstract

In most real data analysis situations, researchers consider several statistical models that might be appropriate for the application. They establish criteria for determining which of the candidate models is best, and whether even that model is good enough to use as the basis for inference. This chapter considers Bayesian methods of comparing models, testing hypotheses, and assessing model adequacy.

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Authors and Affiliations

  1. Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa, USA

    Mary Kathryn Cowles

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  1. Mary Kathryn Cowles
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Cowles, M.K. (2013). Model Comparison, Model Checking, and Hypothesis Testing. In: Applied Bayesian Statistics. Springer Texts in Statistics, vol 98. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5696-4_11

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