Generalizations of the familywise error rate

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Generalizations of the Familywise Error Rate

Consider the problem of simultaneously testing null hypotheses H1, . . . ,Hs. The usual approach to dealing with the multiplicity problem is to restrict attention to procedures that control the familywise error rate (FWER), the probability of even one false rejection. In many applications, particularly if s is large, one might be willing to tolerate more than one false rejection provided the nu...

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Stepup Procedures for Control of Generalizations of the Familywise Error Rate

Consider the multiple testing problem of testing null hypotheses H1, . . . ,Hs. A classical approach to dealing with the multiplicity problem is to restrict attention to procedures that control the familywise error rate (FWER), the probability of even one false rejection. But if s is large, control of the FWER is so stringent that the ability of a procedure that controls the FWER to detect fals...

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Stepup Procedures for Control of Generalizations of the Familywise Error Rate by Joseph P. Romano

The Annals of Statistics 2006, Vol. 0, No. 00, 1–26 DOI: 10.1214/009053606000000461 © Institute of Mathematical Statistics, 2006 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 STEPUP PROCEDURES FOR CONTRO...

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Familywise Error Rate Control via Knockoffs

We present a novel method for controlling the k-familywise error rate (k-FWER) in the linear regression setting using the knockoffs framework first introduced by Barber and Candès. Our procedure, which we also refer to as knockoffs, can be applied with any design matrix with at least as many observations as variables, and does not require knowing the noise variance. Unlike other multiple testin...

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Familywise Error Rate Control via Knockoffs

We present a novel method for controlling the k-familywise error rate (k-FWER) in the linear regression setting using the knockoffs framework first introduced by Barber and Candès. Our procedure, which we also refer to as knockoffs, can be applied with any design matrix with at least as many observations as variables, and does not require knowing the noise variance. Unlike other multiple testin...

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ژورنال

عنوان ژورنال: The Annals of Statistics

سال: 2005

ISSN: 0090-5364

DOI: 10.1214/009053605000000084