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March 2014 Marginal Posterior Simulation via Higher-order Tail Area Approximations
Erlis Ruli, Nicola Sartori, Laura Ventura
Bayesian Anal. 9(1): 129-146 (March 2014). DOI: 10.1214/13-BA851

Abstract

A new method for posterior simulation is proposed, based on the combination of higher-order asymptotic results with the inverse transform sampler. This method can be used to approximate marginal posterior distributions, and related quantities, for a scalar parameter of interest, even in the presence of nuisance parameters. Compared to standard Markov chain Monte Carlo methods, its main advantages are that it gives independent samples at a negligible computational cost, and it allows prior sensitivity analyses under the same Monte Carlo variation. The method is illustrated by a genetic linkage model, a normal regression with censored data and a logistic regression model.

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Erlis Ruli. Nicola Sartori. Laura Ventura. "Marginal Posterior Simulation via Higher-order Tail Area Approximations." Bayesian Anal. 9 (1) 129 - 146, March 2014. https://doi.org/10.1214/13-BA851

Information

Published: March 2014
First available in Project Euclid: 24 February 2014

zbMATH: 1327.62159
MathSciNet: MR3188302
Digital Object Identifier: 10.1214/13-BA851

Rights: Copyright © 2014 International Society for Bayesian Analysis

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Vol.9 • No. 1 • March 2014
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