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A Bayesian \(\chi^2\) test for goodness-of-fit. (English) Zbl 1068.62028

Summary: This article describes an extension of classical \(\chi^2\) goodness-of-fit tests to Bayesian model assessment. The extension, which essentially involves evaluating Pearson’s goodness-of-fit statistic at a parameter value drawn from its posterior distribution, has the important property that it is asymptotically distributed as a \(\chi^2\) random variable on \(K-1\) degrees of freedom, independently of the dimension of the underlying parameter vector.
By examining the posterior distribution of this statistic, global goodness-of-fit diagnostics are obtained. Advantages of these diagnostics include ease of interpretation, computational convenience and favorable power properties. The proposed diagnostics can be used to assess the adequacy of a broad class of Bayesian models, essentially requiring only a finite-dimensional parameter vector and conditionally independent observations.

MSC:

62F15 Bayesian inference
62G10 Nonparametric hypothesis testing
62C10 Bayesian problems; characterization of Bayes procedures
62E20 Asymptotic distribution theory in statistics

Software:

WinBUGS; Gibbsit
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References:

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