Robust Bayesian analysis of selection models

• 成果类型: Article
• 署名作者: Bayarri, MJ; Berger, J
• 署名单位: University of Valencia; Duke University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1998
• 页码: 645-659
• 关键词: sensitivity

摘要

Selection models arise when the data are selected to enter the sample only if they occur in a certain region of the sample space. When this selection occurs according to some probability distribution, the resulting model is often instead called a weighted distribution model. In either case the original density becomes multiplied by a weight function w(x). Often there is considerable uncertainty concerning this weight function; for instance, it may be known only that w Lies between two specified weight functions. We consider robust Bayesian analysis for this situation, finding the range of posterior quantities of interest, such as the posterior mean or posterior probability of a set, as w ranges over the class of weight functions. The variational analysis utilizes concepts from variation diminishing transformations.