Approximate Bayesian inference for quantites
Journal Article (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