Math @ Duke

Publications [#257938] of David B. Dunson
search arxiv.org.Papers Published
 Dunson, DB; Taylor, JA, Approximate Bayesian inference for quantites,
Journal of Nonparametric Statistics, vol. 17 no. 3
(2005),
pp. 385400 [doi]
(last updated on 2018/11/20)
Abstract: 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.


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