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dc.contributor.authorBaddeley, Adrian
dc.contributor.authorHardegen, A.
dc.contributor.authorLawrence, T.
dc.contributor.authorMilne, R.
dc.contributor.authorNair, G.
dc.contributor.authorRakshit, Suman
dc.date.accessioned2018-02-06T06:14:44Z
dc.date.available2018-02-06T06:14:44Z
dc.date.created2018-02-06T05:50:00Z
dc.date.issued2017
dc.identifier.citationBaddeley, A. and Hardegen, A. and Lawrence, T. and Milne, R. and Nair, G. and Rakshit, S. 2017. On two-stage Monte Carlo tests of composite hypotheses. Computational Statistics and Data Analysis. 114: pp. 75-87.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/63012
dc.identifier.doi10.1016/j.csda.2017.04.003
dc.description.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.

dc.publisherElsevier Science
dc.titleOn two-stage Monte Carlo tests of composite hypotheses
dc.typeJournal Article
dcterms.source.volume114
dcterms.source.startPage75
dcterms.source.endPage87
dcterms.source.issn0167-9473
dcterms.source.titleComputational Statistics and Data Analysis
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusFulltext not available


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