Sequential tests controlling generalized familywise error rates

De, Shyamal K.; Baron, Michael

doi:10.1016/j.stamet.2014.10.001

Cited by 14 publications

(14 citation statements)

References 23 publications

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“…The threshold b is selected to guarantee the desired error control. When k = 1, δ S (b) coincides with the Intersection rule, δ I (b, b), defined in (10). When k > 1, the two rules are different but share a similar flavor, since δ S (b) stops the first time n that all sums j∈B |λ j (n)| with B ⊂ [J] and |B| = k are simultaneously above b.…”

Section: Consequently the Multiple Testing Problem Is Symmetric Andmentioning

confidence: 93%

“…Various sequential procedures have been proposed recently to control such generalized familywise error rates [1,2,3,8,9,10]. To the best of our knowledge, the efficiency of these procedures is understood only in the case of classical familywise error rates, i.e., when k 1 = k 2 = 1.…”

mentioning

confidence: 99%

“…The first one is a generalization of the usual mis-classification rate [24,26], where the probability of at least k ≥ 1 mistakes, of any kind, is controlled. The second one controls generalized familywise error rates of both types [1,10], i.e., the probabilities of at least k 1 ≥ 1 false positives and at least k 2 ≥ 1 false negatives.…”

mentioning

confidence: 99%

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Sequential multiple testing with generalized error control: An asymptotic optimality theory

Song¹,

Fellouris²

2019

Ann. Statist.

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The sequential multiple testing problem is considered under two generalized error metrics. Under the first one, the probability of at least k mistakes, of any kind, is controlled. Under the second, the probabilities of at least k1 false positives and at least k2 false negatives are simultaneously controlled. For each formulation, the optimal expected sample size is characterized, to a first-order asymptotic approximation as the error probabilities go to 0, and a novel multiple testing procedure is proposed and shown to be asymptotically efficient under every signal configuration. These results are established when the data streams for the various hypotheses are independent and each local log-likelihood ratio statistic satisfies a certain Strong Law of Large Numbers. In the special case of i.i.d. observations in each stream, the gains of the proposed sequential procedures over fixed-sample size schemes are quantified. MSC 2010 subject classifications: Primary 62L10

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Section: Consequently the Multiple Testing Problem Is Symmetric Andmentioning

confidence: 93%

mentioning

confidence: 99%

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Sequential multiple testing with generalized error control: An asymptotic optimality theory

Song¹,

Fellouris²

2019

Ann. Statist.

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“…A simple method is to run one-sided truncated sequential tests by just finding the true changed panels as discussed in Wu (2018). Further discussions on sequential multiple tests on controlling FDR can also be used in the supplementary runs; see Bartroff (2017), De and Baron (2015), and Song and Fellouris (2019). As discussed in Wu (2019), we may also use the adaptive combined SR-CUSUM procedure which can eliminate large biases of the common change point estimation when the post change means are unknown.…”

Section: Discussionmentioning

confidence: 99%

Estimation of common change point and isolation of changed panels after sequential detection

2020

Sequential Analysis

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Quick detection of common changes is critical in sequential monitoring of multi-stream data where a common change is referred as a change that only occurs in a portion of panels. After a common change is detected by using a combined CUSUM-SR procedure, we first study the joint distribution for values of the CUSUM process and the estimated delay detection time for the unchanged panels. The BH method by using the asymptotic exponential property for the CUSUM process is developed to isolate the changed panels with the control on FDR. The common change point is then estimated based on the isolated changed panels. Simulation results show that the proposed method can also control the FNR by properly selecting FDR.

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“…Enhancing the Bonferroni method, Holm [15] proposed a scheme based on the ordered p-values. Developing upon Holm's idea, step-up and step-down methods for multiple testing have been developed for non-sequential [11,[16][17][18][19] and most recently, sequential experiments [20][21][22][23]. These Holm-type methods (also called stepwise for testing marginal hypotheses in the order of their significance) allow to use higher levels of j α leading to increased power, while still controlling FWER.…”

Section: Introductionmentioning

confidence: 99%

Weighted Statistics for Testing Multiple Endpoints in Clinical Trials

Baron

2019

ABBA

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Bonferroni, Holm, and Holm-type stepwise approaches have been well developed for the simultaneous testing of multiple hypotheses in medical experiments. Methods exist for controlling familywise error rates at their preset levels. This article shows how performance of these tests can often be substantially improved by accounting for the relative difficulty of tests. Introducing suitably chosen weights optimizes the error spending between the multiple endpoints. Such an extension of classical testing schemes generally results in a smaller required sample size without sacrificing the familywise error rate and power.

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Sequential tests controlling generalized familywise error rates

Cited by 14 publications

References 23 publications

Sequential multiple testing with generalized error control: An asymptotic optimality theory

Sequential multiple testing with generalized error control: An asymptotic optimality theory

Estimation of common change point and isolation of changed panels after sequential detection

Weighted Statistics for Testing Multiple Endpoints in Clinical Trials

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