Improving p-value approximation and level accuracy of Monte Carlo tests by quasi-Monte Carlo methods

Sung Nok CHIU*, Kwong Ip LIU

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We argue and show empirically that for the Monte Carlo test, if the pseudo-random numbers are replaced by a randomized low discrepancy sequence, the actual errors in approximating the p-value are smaller and the deviations of the exact level from the nominal level have higher potential to be smaller. Hence in real applications the proposed method, called randomized quasi-Monte Carlo test, is suggested to be used instead of the traditional Monte Carlo test.

Original languageEnglish
Pages (from-to)1272-1288
Number of pages17
JournalCommunications in Statistics Part B: Simulation and Computation
Volume51
Issue number3
Early online date20 Sep 2019
DOIs
Publication statusPublished - Mar 2022

Scopus Subject Areas

  • Statistics and Probability
  • Modelling and Simulation

User-Defined Keywords

  • Monte Carlo test
  • Quasi-Monte Carlo
  • Resampling
  • Sobol’ sequence

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