On two-stage Monte Carlo tests of composite hypotheses

Adrian Baddeley, Andrew Hardegen, Thomas Lawrence, Robin K. Milne, Gopalan Nair, Suman Rakshit

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

A major weakness of the classical Monte Carlo test is that it is biased when the null hypothesis is composite. This problem persists even when the number of simulations tends to infinity. A standard remedy is to perform a double bootstrap test involving two stages of Monte Carlo simulation: under suitable conditions, this test is asymptotically exact for any fixed significance level. However, the two-stage test is shown to perform poorly in some common applications: for a given number of simulations, the test with the smallest achievable significance level can be strongly biased. A ‘balanced’ version of the two-stage test is proposed, which is exact, for all achievable significance levels, when the null hypothesis is simple, and which performs well for composite null hypotheses.

Original languageEnglish
Pages (from-to)75-87
Number of pages13
JournalComputational Statistics and Data Analysis
Volume114
DOIs
Publication statusPublished - 1 Oct 2017

Fingerprint

Dive into the research topics of 'On two-stage Monte Carlo tests of composite hypotheses'. Together they form a unique fingerprint.

Cite this