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Two new data-dependent choices of m when applying the m-out-of-n bootstrap to hypothesis testing

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dc.contributor.author Allison, James Samuel
dc.contributor.author Santana, Leonard
dc.contributor.author Swanepoel, Jan Willem Hendrik
dc.date.accessioned 2012-11-01T05:16:24Z
dc.date.available 2012-11-01T05:16:24Z
dc.date.issued 2011
dc.identifier.citation Allison, J.S. et al. 2011. Two new data-dependent choices of m when applying the m-out-of-n bootstrap to hypothesis testing. Journal of statistical computation and simulation, 81(12):2107-2120. [http://www.tandfonline.com/toc/gscs20/current] en_US
dc.identifier.issn 0094-9655
dc.identifier.issn 1563-5163 (Online)
dc.identifier.uri http://hdl.handle.net/10394/7695
dc.description.abstract 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. en_US
dc.description.uri http://dx.doi.org/10.1080/00949655.2010.519338
dc.language.iso en en_US
dc.publisher Taylor & Francis en_US
dc.subject m-out-of-n bootstrap en_US
dc.subject resample size selection en_US
dc.subject hypothesis test en_US
dc.subject critical value en_US
dc.subject p-value en_US
dc.title Two new data-dependent choices of m when applying the m-out-of-n bootstrap to hypothesis testing en_US
dc.type Article en_US


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