Open Access
2018 Exact and efficient inference for partial Bayes problems
Yixuan Qiu, Lingsong Zhang, Chuanhai Liu
Electron. J. Statist. 12(2): 4640-4668 (2018). DOI: 10.1214/18-EJS1511


Bayesian methods are useful for statistical inference. However, real-world problems can be challenging using Bayesian methods when the data analyst has only limited prior knowledge. In this paper we consider a class of problems, called partial Bayes problems, in which the prior information is only partially available. Taking the recently proposed inferential model approach, we develop a general inference framework for partial Bayes problems, and derive both exact and efficient solutions. In addition to the theoretical investigation, numerical results and real applications are used to demonstrate the superior performance of the proposed method.


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Yixuan Qiu. Lingsong Zhang. Chuanhai Liu. "Exact and efficient inference for partial Bayes problems." Electron. J. Statist. 12 (2) 4640 - 4668, 2018.


Received: 1 March 2018; Published: 2018
First available in Project Euclid: 21 December 2018

zbMATH: 07003253
MathSciNet: MR3893423
Digital Object Identifier: 10.1214/18-EJS1511

Keywords: confidence distribution , Empirical Bayes , Exact inference , inferential model , partial prior

Vol.12 • No. 2 • 2018
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