Electronic Journal of Statistics

Exact and efficient inference for partial Bayes problems

Yixuan Qiu, Lingsong Zhang, and Chuanhai Liu

Full-text: Open access

Abstract

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.

Article information

Source
Electron. J. Statist., Volume 12, Number 2 (2018), 4640-4668.

Dates
Received: March 2018
First available in Project Euclid: 21 December 2018

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1545382952

Digital Object Identifier
doi:10.1214/18-EJS1511

Mathematical Reviews number (MathSciNet)
MR3893423

Keywords
Confidence distribution empirical Bayes exact inference inferential model partial prior

Rights
Creative Commons Attribution 4.0 International License.

Citation

Qiu, Yixuan; Zhang, Lingsong; Liu, Chuanhai. Exact and efficient inference for partial Bayes problems. Electron. J. Statist. 12 (2018), no. 2, 4640--4668. doi:10.1214/18-EJS1511. https://projecteuclid.org/euclid.ejs/1545382952


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