{"title":"Frequentist Bayesian compound inference","authors":"Jinfeng Xu, Ao Yuan","doi":"10.4310/23-sii797","DOIUrl":null,"url":null,"abstract":"In practice often either the Bayesian or frequentist method is used, although there are some combined uses of the two methods, a formal unified methodology of the two hasn’t been seen. Here we first give a brief review of the two methods and some combination of the two, then propose a procedure using both the frequentist likelihood and the Bayesian posterior loss in parameter estimation and hypothesis testing, as an attempt to unify the two methods. Basic properties of the proposed method are studied, and simulation studies are carried out to evaluate the performance of the method.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"8 3","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics and Its Interface","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.4310/23-sii797","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
In practice often either the Bayesian or frequentist method is used, although there are some combined uses of the two methods, a formal unified methodology of the two hasn’t been seen. Here we first give a brief review of the two methods and some combination of the two, then propose a procedure using both the frequentist likelihood and the Bayesian posterior loss in parameter estimation and hypothesis testing, as an attempt to unify the two methods. Basic properties of the proposed method are studied, and simulation studies are carried out to evaluate the performance of the method.
期刊介绍:
Exploring the interface between the field of statistics and other disciplines, including but not limited to: biomedical sciences, geosciences, computer sciences, engineering, and social and behavioral sciences. Publishes high-quality articles in broad areas of statistical science, emphasizing substantive problems, sound statistical models and methods, clear and efficient computational algorithms, and insightful discussions of the motivating problems.