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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Chicago
ICMJE
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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, Berger JO. Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence. Bayesian Analysis. 2020 Jan 1;16(1):111–28.
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.
Chang S, Berger JO. Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence. Bayesian Analysis. 2020 Jan 1;16(1):111–128.
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