期刊
JOURNAL OF NONPARAMETRIC STATISTICS
卷 17, 期 3, 页码 385-400出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/10485250500039049
关键词
comet assay; nonparametric; median regression; order constraints; prior elicitation; quantile regression; single-cell electrophoresis; substitution likelihood
Suppose data consist of a random sample from a distribution function F-gamma, which is unknown, and that interest focuses on inferences on theta, a vector of quantiles of F-gamma. 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.
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