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Metrika

, Volume 71, Issue 2, pp 125–138 | Cite as

Optimal hypothesis testing: from semi to fully Bayes factors

  • Albert Vexler
  • Chengqing Wu
  • Kai Fun YuEmail author
Article

Abstract

We propose and examine statistical test-strategies that are somewhat between the maximum likelihood ratio and Bayes factor methods that are well addressed in the literature. The paper shows an optimality of the proposed tests of hypothesis. We demonstrate that our approach can be easily applied to practical studies, because execution of the tests does not require deriving of asymptotical analytical solutions regarding the type I error. However, when the proposed method is utilized, the classical significance level of tests can be controlled.

Keywords

Likelihood ratio Maximum likelihood Bayes factor Most powerful Hypotheses testing Significance level Type I error 

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of BiostatisticsThe New York State University at BuffaloBuffaloUSA
  2. 2.Department of Epidemiology and Public HealthYale UniversityNew HavenUSA
  3. 3.Department of Health and Human ServicesBiostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaUSA

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