Efficient p-value evaluation for resampling-based tests
نویسندگان
چکیده
منابع مشابه
Efficient p-value evaluation for resampling-based tests.
The resampling-based test, which often relies on permutation or bootstrap procedures, has been widely used for statistical hypothesis testing when the asymptotic distribution of the test statistic is unavailable or unreliable. It requires repeated calculations of the test statistic on a large number of simulated data sets for its significance level assessment, and thus it could become very comp...
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Monte Carlo estimation of the power of tests that require resampling can be very com-putationally intensive. It is possible to reduce the size of the inner resampling loop as long as the resulting estimator of power can be corrected for bias. A simple linear extrapolation method is shown to perform well in correcting for bias and thus reduces computation time in Monte Carlo power studies.
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There has been a recent interest in reporting p-values adjusted for resampling-based stepdown multiple testing procedures proposed in Romano and Wolf (2005a,b). The original papers only describe how to carry out multiple testing at a fixed significance level. Computing adjusted p-values instead in an efficient manner is not entirely trivial. Therefore, this paper fills an apparent gap by detail...
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Bootstrap principle is briefly reviewed. Hall’s (1989) antithetic variates method for bootstrap is discussed and extended to more than two antithetic resampling processes. We illustrate the theory with a simulation study. The numerical results show that increasing the number of antithetic resampling processes produces significant smaller variances of the bootstrap estimator over the paired case.
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ژورنال
عنوان ژورنال: Biostatistics
سال: 2011
ISSN: 1465-4644,1468-4357
DOI: 10.1093/biostatistics/kxq078