文章基本信息

标题：New Classes of Priors Based on Stochastic Orders and Distortion Functions
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作者：J. Pablo Arias-Nicolás ; Fabrizio Ruggeri ; Alfonso Suárez-Llorens 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2016
卷号：11
期号：4
页码：1107-1136
DOI：10.1214/15-BA984
语种：English
出版社：International Society for Bayesian Analysis
摘要：In the context of robust Bayesian analysis, we introduce a new class of prior distributions based on stochastic orders and distortion functions. We provide the new definition, its interpretation and the main properties and we also study the relationship with other classical classes of prior beliefs. We also consider Kolmogorov and Kantorovich metrics to measure the uncertainty induced by such a class, as well as its effect on the set of corresponding Bayes actions. Finally, we conclude the paper with some numerical examples.