Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#257938] of David B. Dunson


Papers Published

  1. Dunson, DB; Taylor, JA, Approximate Bayesian inference for quantites, Journal of Nonparametric Statistics, vol. 17 no. 3 (2005), pp. 385-400 [doi]
    (last updated on 2017/12/11)

    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.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320