A general guide in Bayesian and robust Bayesian estimation using Dirichlet processes

被引：0
|
作者：
Ali Karimnezhad
Mahmoud Zarepour
机构：
[1] University of Ottawa,Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine
[2] University of Ottawa,Department of Statistics
来源：
Metrika | 2020年 / 83卷
关键词：
Bayesian estimation; Bayesian nonparametrics; Dirichlet process; Dirichlet invariant process;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we investigate Bayesian and robust Bayesian estimation of a wide range of parameters of interest in the context of Bayesian nonparametrics under a broad class of loss functions. Dealing with uncertainty regarding the prior, we consider the Dirichlet and the Dirichlet invariant priors, and provide explicit form of the resulting Bayes and robust Bayes estimators. Tractability of the results is supported by numerous examples of different well-known loss functions. The practical utility of the proposed Bayes and robust Bayes estimators are examined for a real data set.
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页码：321 / 346
页数：25
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