On using truncated sequential probability ratio test boundaries for Monte Carlo implementation of hypothesis tests

Michael P. Fay, Hyune Ju Kim, Mark Hachey

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

When designing programs or software for the implementation of Monte Carlo (MC) hypothesis tests, we can save computation time by using sequential stopping boundaries. Such boundaries imply stopping resampling after relatively few replications if the early replications indicate a very large or a very small p value. We study a truncated sequential probability ratio test (SPRT) boundary and provide a tractable algorithm to implement it. We review two properties desired of any MC p value, the validity of the p value and a small resampling risk, where resampling risk is the probability that the accept/reject decision will be different than the decision from complete enumeration. We show how the algorithm can be used to calculate a valid p value and confidence intervals for any truncated SPRT boundary. We show that a class of SPRT boundaries is minimax with respect to resampling risk and recommend a truncated version of boundaries in that class by comparing their resampling risk (RR) to the RR of fixed boundaries with the same maximum resample size. We study the lack of validity of some simple estimators of p values and offer a new, simple valid p value for the recommended truncated SPRT boundary. We explore the use of these methods in a practical example and provide the MChtest R package to perform the methods.

Original languageEnglish (US)
Pages (from-to)946-967
Number of pages22
JournalJournal of Computational and Graphical Statistics
Volume16
Issue number4
DOIs
StatePublished - Dec 1 2007

Keywords

  • B-value
  • Bootstrap
  • Permutation
  • Resampling risk
  • Sequential design
  • Sequential probability ratio test

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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