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首页> 外文期刊>Journal of statistical computation and simulation >Two new data-dependent choices of m when applying the m-out-of-n bootstrap to hypothesis testing
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Two new data-dependent choices of m when applying the m-out-of-n bootstrap to hypothesis testing

机译：将m-out-of-n引导程序应用于假设检验时，m的两个新的数据相关选择

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摘要

The traditional non-parametric bootstrap (referred to as the n-out-of-n bootstrap) is a widely applicable and powerful tool for statistical inference, but in important situations it can fail. It is well known that by using a bootstrap sample of size m, different from n, the resulting m-out-of-n bootstrap provides a method for rectifying the traditional bootstrap inconsistency. Moreover, recent studies have shown that interesting cases exist where it is better to use the m-out-of-n bootstrap in spite of the fact that the n-out-of-n bootstrap works. In this paper, we discuss another case by considering its application to hypothesis testing. Two new data-based choices of m are proposed in this set-up. The results of simulation studies are presented to provide empirical comparisons between the performance of the traditional bootstrap and the m-out-of-n bootstrap, based on the two data-dependent choices of m, as well as on an existing method in the literature for choosing m. These results show that the m-out-of-n bootstrap, based on our choice of m, generally outperforms the traditional bootstrap procedure as well as the procedure based on the choice of m proposed in the literature.
机译：传统的非参数引导程序（称为n-out-of-n引导程序）是用于统计推断的广泛适用且功能强大的工具，但在重要情况下，它可能会失败。众所周知，通过使用大小为m且不同于n的引导程序样本，所得的n出n的引导程序提供了纠正传统引导程序不一致的方法。此外，最近的研究表明，存在有趣的情况，尽管n-out-of-n引导程序起作用，但最好使用m-out-of-n引导程序。在本文中，我们通过考虑其在假设检验中的应用来讨论另一种情况。在此设置中，提出了m的两个基于数据的新选择。提出了仿真研究的结果，以基于m的两个数据相关选择以及文献中的现有方法，提供了传统引导程序和m-out-of-n引导程序性能的经验比较。选择米这些结果表明，基于我们对m的选择，m出n的引导程序通常优于传统的引导程序以及文献中提出的基于m的选择程序。

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