Approximate Bayesian inference for quantites

Published

Journal Article

Suppose data consist of a random sample from a distribution function F Y, which is unknown, and that interest focuses on inferences on θ, a vector of quantiles of FY. When the likelihood function is not fully specified, a posterior density cannot be calculated and Bayesian inference is difficult. This article considers an approach which relies on a substitution likelihood characterized by a vector of quantiles. Properties of the substitution likelihood are investigated, strategies for prior elicitation are presented, and a general framework is proposed for quantile regression modeling. Posterior computation proceeds via a Metropolis algorithm that utilizes a normal approximation to the posterior. Results from a simulation study are presented, and the methods are illustrated through application to data from a genotoxicity experiment. © 2005 Taylor & Francis Ltd.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB; Taylor, JA

Published Date

  • April 1, 2005

Published In

Volume / Issue

  • 17 / 3

Start / End Page

  • 385 - 400

International Standard Serial Number (ISSN)

  • 1048-5252

Digital Object Identifier (DOI)

  • 10.1080/10485250500039049

Citation Source

  • Scopus