Statistical Science

Bayes Model Selection with Path Sampling: Factor Models and Other Examples

Ritabrata Dutta and Jayanta K. Ghosh

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We prove a theorem justifying the regularity conditions which are needed for Path Sampling in Factor Models. We then show that the remaining ingredient, namely, MCMC for calculating the integrand at each point in the path, may be seriously flawed, leading to wrong estimates of Bayes factors. We provide a new method of Path Sampling (with Small Change) that works much better than standard Path Sampling in the sense of estimating the Bayes factor better and choosing the correct model more often. When the more complex factor model is true, PS-SC is substantially more accurate. New MCMC diagnostics is provided for these problems in support of our conclusions and recommendations. Some of our ideas for diagnostics and improvement in computation through small changes should apply to other methods of computation of the Bayes factor for model selection.

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Statist. Sci., Volume 28, Number 1 (2013), 95-115.

First available in Project Euclid: 29 January 2013

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Bayes model selection covariance models path sampling Laplace approximation


Dutta, Ritabrata; Ghosh, Jayanta K. Bayes Model Selection with Path Sampling: Factor Models and Other Examples. Statist. Sci. 28 (2013), no. 1, 95--115. doi:10.1214/12-STS403.

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