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Finite Sample Properties and Asymptotic Efficiency of Monte Carlo Tests
The Annals of Statistics
Vol. 14, No. 1 (Mar., 1986), pp. 336-347
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2241285
Page Count: 12
You can always find the topics here!Topics: Simulations, Statism, Sample size, Null hypothesis, Permutation tests, Statistics, Topological theorems, Approximation, Hope, Infinity
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Since their introduction by Dwass (1957) and Barnard (1963), Monte Carlo tests have attracted considerable attention. The aim of this paper is to give a unified approach that covers the case of an arbitrary null distribution in order to study the statistical properties of Monte Carlo tests under the null hypothesis and under the alternative. For finite samples we obtain bounds for the power of the Monte Carlo test with the original test that allow determination of the required simulation effort. Furthermore the concept of asymptotic (resp. local asymptotic) relative Pitman efficiency (ARPE, resp. LARPE) is adapted to Monte Carlo tests for the study of their asymptotic behaviour. The normal limit case is investigated in more detail, leading to explicit formulas for ARPE and LARPE.
The Annals of Statistics © 1986 Institute of Mathematical Statistics