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dc.contributor.authorAllison, James Samuel
dc.contributor.authorSantana, Leonard
dc.contributor.authorSwanepoel, Jan Willem Hendrik
dc.date.accessioned2012-11-01T05:16:24Z
dc.date.available2012-11-01T05:16:24Z
dc.date.issued2011
dc.identifier.citationAllison, 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.issn0094-9655
dc.identifier.issn1563-5163 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/7695
dc.description.abstractThe 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.urihttp://dx.doi.org/10.1080/00949655.2010.519338
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.subjectm-out-of-n bootstrapen_US
dc.subjectresample size selectionen_US
dc.subjecthypothesis testen_US
dc.subjectcritical valueen_US
dc.subjectp-valueen_US
dc.titleTwo new data-dependent choices of m when applying the m-out-of-n bootstrap to hypothesis testingen_US
dc.typeArticleen_US


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