AN ALGORITHM TO COMPUTE THE POWER OF MONTE CARLO TESTS WITH GUARANTEED PRECISION
成果类型：
Article
署名作者：
Gandy, Axel; Rubin-Delanchy, Patrick
署名单位：
Imperial College London; University of Bristol
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/12-AOS1076
发表日期：
2013
页码：
125-142
关键词：
摘要：
This article presents an algorithm that generates a conservative confidence interval of a specified length and coverage probability for the power of a Monte Carlo test (such as a bootstrap or permutation test). It is the first method that achieves this aim for almost any Monte Carlo test. Previous research has focused on obtaining as accurate a result as possible for a fixed computational effort, without providing a guaranteed precision in the above sense. The algorithm we propose does not have a fixed effort and runs until a confidence interval with a user-specified length and coverage probability can be constructed. We show that the expected effort required by the algorithm is finite in most cases of practical interest, including situations where the distribution of the p-value is absolutely continuous or discrete with finite support. The algorithm is implemented in the R-package simctest, available on CRAN.
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