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Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence

Publication ,  Journal Article
Chang, S; Berger, JO
Published in: Bayesian Analysis
January 1, 2020

The problem of testing mutually exclusive hypotheses with dependent test statistics is considered. Bayesian and frequentist approaches to multiplicity control are studied and compared to help gain understanding as to the effect of test statistic dependence on each approach. The Bayesian approach is shown to have excellent frequentist properties and is argued to be the most effective way of obtaining frequentist multiplicity control, without sacrificing power, when there is considerable test statistic dependence.

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Published In

Bayesian Analysis

DOI

EISSN

1931-6690

ISSN

1936-0975

Publication Date

January 1, 2020

Volume

16

Issue

1

Start / End Page

111 / 128

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0104 Statistics
 

Citation

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Chang, S., & Berger, J. O. (2020). Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence. Bayesian Analysis, 16(1), 111–128. https://doi.org/10.1214/20-BA1196
Chang, S., and J. O. Berger. “Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence.” Bayesian Analysis 16, no. 1 (January 1, 2020): 111–28. https://doi.org/10.1214/20-BA1196.
Chang, S., and J. O. Berger. “Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence.” Bayesian Analysis, vol. 16, no. 1, Jan. 2020, pp. 111–28. Scopus, doi:10.1214/20-BA1196.

Published In

Bayesian Analysis

DOI

EISSN

1931-6690

ISSN

1936-0975

Publication Date

January 1, 2020

Volume

16

Issue

1

Start / End Page

111 / 128

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0104 Statistics