Objective priors for the bivariate normal model
The Annals of Statistics · 2008 · Volume 36 · Issue 2
Abstract
Study of the bivariate normal distribution raises the full range of issues involving objective Bayesian inference, including the different types of objective priors (e.g., Jeffreys, invariant, reference, matching), the different modes of inference (e.g., Bayesian, frequentist, fiducial) and the criteria involved in deciding on optimal objective priors (e.g., ease of computation, frequentist performance, marginalization paradoxes). Summary recommendations as to optimal objective priors are made for a variety of inferences involving the bivariate normal distribution. In the course of the investigation, a variety of surprising results were found, including the availability of objective priors that yield exact frequentist inferences for many functions of the bivariate normal parameters, including the correlation coefficient.
Keywords
62A01, 62E15, 62F10, 62F15, 62F25, 62H10, 62H20, constructive posterior distributions, Fiducial inference, frequentist coverage, Jeffreys priors, marginalization paradox, matching priors, reference priors, rejection sampling, right-Haar prior
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