Computational approaches for empirical Bayes methods and Bayesian sensitivity analysis

Abstract

We consider situations in Bayesian analysis where we have a family of priors ${\nu }_h$ on the parameter $\theta$, where $h$ varies continuously over a space $\mathcal{H}$, and we deal with two related problems. The first involves sensitivity analysis and is stated as follows. Suppose we fix a function $f$ of $\theta$. How do we efficiently estimate the posterior expectation of $f(\theta )$ simultaneously for all $h$ in $\mathcal{H}$? The second problem is how do we identify subsets of $\mathcal{H}$ which give rise to reasonable choices of ${\nu }_h$? We assume that we are able to generate Markov chain samples from the posterior for a finite number of the priors, and we develop a methodology, based on a combination of importance sampling and the use of control variates, for dealing with these two problems. The methodology applies very generally, and we show how it applies in particular to a commonly used model for variable selection in Bayesian linear regression, and give an illustration on the US crime data of Vandaele.

Published in at http://dx.doi.org/10.1214/11-AOS913 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (2)

• Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
• Computational approaches for empirical Bayes methods and Bayesian sensitivity analysis

Cited by in corpus (1)

• Computational approaches for empirical Bayes methods and Bayesian sensitivity analysis