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Posterior Predictive Assessment of Model Fitness Via Realized Discrepancies (1996)
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| Venue: | Statistica Sinica |
| Citations: | 348 - 39 self |
BibTeX
@ARTICLE{Gelman96posteriorpredictive,
author = {Andrew Gelman and Xiao-li Meng and Hal Stern},
title = {Posterior Predictive Assessment of Model Fitness Via Realized Discrepancies},
journal = {Statistica Sinica},
year = {1996},
pages = {733--807}
}
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Abstract
Abstract: This paper considers Bayesian counterparts of the classical tests for goodness of fit and their use in judging the fit of a single Bayesian model to the observed data. We focus on posterior predictive assessment, in a framework that also includes conditioning on auxiliary statistics. The Bayesian formulation facilitates the construction and calculation of a meaningful reference distribution not only for any (classical) statistic, but also for any parameter-dependent “statistic ” or discrepancy. The latter allows us to propose the realized discrepancy assessment of model fitness, which directly measures the true discrepancy between data and the posited model, for any aspect of the model which we want to explore. The computation required for the realized discrepancy assessment is a straightforward byproduct of the posterior simulation used for the original Bayesian analysis. We illustrate with three applied examples. The first example, which serves mainly to motivate the work, illustrates the difficulty of classical tests in assessing the fitness of a Poisson model to a positron emission tomography image that is constrained to be nonnegative. The second and third examples illustrate the details of the posterior predictive approach in two problems: estimation in a model with inequality constraints on the parameters, and estimation in a mixture model. In all three examples, standard test statistics (either a χ 2 or a likelihood ratio) are not pivotal: the difficulty is not just how to compute the reference distribution for the test, but that in the classical framework no such distribution exists, independent of the unknown model parameters. Key words and phrases: Bayesian p-value, χ 2 test, discrepancy, graphical assessment, mixture model, model criticism, posterior predictive p-value, prior predictive
Keyphrases
posterior predictive assessment model fitness via realized discrepancy classical test mixture model realized discrepancy assessment positron emission tomography image posited model unknown model parameter first example posterior simulation poisson model reference distribution third example classical framework standard test statistic bayesian formulation posterior predictive approach auxiliary statistic applied example single bayesian model model fitness bayesian counterpart inequality constraint posterior predictive p-value parameter-dependent statistic straightforward byproduct true discrepancy graphical assessment meaningful reference distribution bayesian p-value distribution exists key word original bayesian analysis likelihood ratio
