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Optimal hypothesis testing: From semi to fully Bayes factors

  • Yale University
  • National Institutes of Health

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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.

Original languageEnglish
Pages (from-to)125-138
Number of pages14
JournalMetrika
Volume71
Issue number2
DOIs
StatePublished - Jan 2010

Keywords

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

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