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Open AccessJournal ArticleDOI

On the Optimality of Some Multiple Comparison Procedures

Emil Spjotvoll
- 01 Apr 1972 - 
- Vol. 43, Iss: 2, pp 398-411
TLDR
In this paper, the optimality criteria formulated in terms of the power functions of individual tests are given for problems where several hypotheses are tested simultaneously, subject to the constraint that the expected number of false rejections is less than a given constant gamma when all null hypotheses are true.
Abstract
: Optimality criteria formulated in terms of the power functions of the individual tests are given for problems where several hypotheses are tested simultaneously. Subject to the constraint that the expected number of false rejections is less than a given constant gamma when all null hypotheses are true, tests are found which maximize the minimum average power and the minimum power of the individual tests over certain alternatives. In the common situations in the analysis of variance this leads to application of multiple t-tests. Recommendations for choosing the value of gamma are given by relating gamma to the probability of no false rejections if all hypotheses are true. Based upon the optimality of the tests, a similar optimality property of joint confidence sets is also derived. (Author)

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Journal ArticleDOI

Controlling the false discovery rate: a practical and powerful approach to multiple testing

TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Journal ArticleDOI

Multiple Hypothesis Testing

TL;DR: In this paper, the first-order Bonferroni inequality and Simes equality were used to control the false discovery rate in a test procedure, and the results showed strong robustness and robustness.
Journal ArticleDOI

Multiparameter Hypothesis Testing and Acceptance Sampling

TL;DR: In this paper, a method of determining whether all the parameters meet their respective standards is proposed, which consists of testing each parameter individually and deciding that the product is acceptable only if each parameter passes its test.
Journal ArticleDOI

Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control

TL;DR: A compound decision theory framework for multiple-testing problems is developed and an oracle rule based on the z values is derived that minimizes the false nondiscovery rate (FNR) and is more efficient than the conventional p value–based methods.
Journal ArticleDOI

Multiple Hypotheses Testing with Weights

TL;DR: In this paper, a multiplicity of approaches and procedures for multiple testing problems with weights are discussed, for both the intersection hypothesis testing and the multiple hypotheses testing problems, and an optimal per family weighted error-rate controlling procedure is obtained.
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