Prior sample size extensions for assessing prior impact and prior-likelihood discordance

Matthew Reimherr, Xiao-Li Meng, Dan L. Nicolae

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2021 · Volume 83 · Issue 3

Abstract

This paper outlines a framework for quantifying the prior’s contribution to posterior inference in the presence of prior-likelihood discordance, a broader concept than the usual notion of prior-likelihood conflict. We achieve this dual purpose by extending the classic notion ofprior sample size,M, in three directions: (I) estimatingMbeyond conjugate families; (II) formulatingMas a relative notion that is as a function of the likelihood sample sizek,M(k), which also leads naturally to a graphical diagnosis; and (III) permitting negativeM, as a measure of prior-likelihood conflict, that is, harmful discordance. Our asymptotic regime permits the prior sample size to grow with the likelihood data size, hence making asymptotic arguments meaningful for investigating the impact of the prior relative to that of likelihood. It leads to a simple asymptotic formula for quantifying the impact of a proper prior that only involves computing a centrality and a spread measure of the prior and the posterior. We use simulated and real data to illustrate the potential of the proposed framework, including quantifying how weak is a ‘weakly informative’ prior adopted in a study of lupus nephritis. Whereas we take a pragmatic perspective in assessing the impact of a prior on a given inference problem under a specific evaluative metric, we also touch upon conceptual and theoretical issues such as using improper priors and permitting priors with asymptotically non-vanishing influence.

Keywords

conjugate prior, prior-likelihood conflict, weakly informative prior

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