Logarithmically efficient simulation for misclassification probabilities in sequential multiple testing

Yanglei Song, Georgios Fellouris

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We consider the problem of estimating via Monte Carlo simulation the misclassification probabilities of two sequential multiple testing procedures. The first one stops when all local test statistics exceed simultaneously either a positive or a negative threshold. The second assumes knowledge of the true number of signals, say m, and stops when the gap between the top m test statistics and the remaining ones exceeds a threshold. For each multiple testing procedure, we propose an importance sampling algorithm for the estimation of its misclassification probability. These algorithms are shown to be logarithmically efficient when the data for the various statistical hypotheses are independent, and each testing problem satisfies an asymptotic stability condition and a symmetry condition. Our theoretical results are illustrated by a simulation study in the special case of testing the drifts of Gaussian random walks.

Original languageEnglish (US)
Title of host publication2016 Winter Simulation Conference
Subtitle of host publicationSimulating Complex Service Systems, WSC 2016
EditorsTheresa M. Roeder, Peter I. Frazier, Robert Szechtman, Enlu Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781509044863
StatePublished - Jul 2 2016
Event2016 Winter Simulation Conference, WSC 2016 - Arlington, United States
Duration: Dec 11 2016Dec 14 2016

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2016 Winter Simulation Conference, WSC 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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