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On invariant moment matching priors for Bayesian point prediction

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  • Hashimoto, Shintaro

Abstract

This paper derives objective priors that asymptotically match the mean of the Bayesian predictive distribution with that of the frequentist plug-in predictive distribution. This moment matching criterion was originally proposed by Ghosh and Liu (2011) for estimation problems; the resulting priors are referred to as moment matching priors. In the predictive context, while the derived priors take a slightly different form from those for estimation, they exhibit a desirable invariance property under one-to-one parameter transformations. This is a feature not typically attained in estimation frameworks. Furthermore, this study characterizes asymptotically unbiased priors for point prediction.

Suggested Citation

  • Hashimoto, Shintaro, 2026. "On invariant moment matching priors for Bayesian point prediction," Statistics & Probability Letters, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:stapro:v:238:y:2026:i:c:s0167715226002385
    DOI: 10.1016/j.spl.2026.110874
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