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dc.contributor.advisorBickis, Mikelis G.en_US
dc.creatorLi, Xiao Bo (Alice)en_US
dc.date.accessioned2008-08-08T18:07:09Zen_US
dc.date.accessioned2013-01-04T04:51:45Z
dc.date.available2009-09-10T08:00:00Zen_US
dc.date.available2013-01-04T04:51:45Z
dc.date.created2008en_US
dc.date.issued2008en_US
dc.date.submitted2008en_US
dc.identifier.urihttp://hdl.handle.net/10388/etd-08082008-180709en_US
dc.description.abstractThis thesis is about multiple hypothesis testing and its relation to the P-value. In Chapter 1, the methodologies of hypothesis testing among the three inference schools are reviewed. Jeffreys, Fisher, and Neyman advocated three different approaches for testing by using the posterior probabilities, P-value, and Type I error and Type II error probabilities respectively. In Berger's words ``Each was quite critical of the other approaches." Berger proposed a potential methodological unified conditional frequentist approach for testing. His idea is to follow Fisher in using the P-value to define the strength of evidence in data and to follow Fisher's method of conditioning on strength of evidence; then follow Neyman by computing Type I and Type II error probabilities conditioning on strength of evidence in the data, which equal the objective posterior probabilities of the hypothesis advocated by Jeffreys. Bickis proposed another estimate on calibrating the null and alternative components of the distribution by modeling the set of P-values as a sample from a mixed population composed of a uniform distribution for the null cases and an unknown distribution for the alternatives. For tackling multiplicity, exploiting the empirical distribution of P-values is applied. A variety of density estimators for calibrating posterior probabilities of the null hypothesis given P-values are implemented. Finally, a noninterpolatory and shape-preserving estimator based on B-splines as smoothing functions is proposed and implemented.en_US
dc.language.isoen_USen_US
dc.subjectmultiple hypothesis tesingen_US
dc.titleMultiple significance tests and their relation to P-valuesen_US
thesis.degree.departmentMathematics and Statisticsen_US
thesis.degree.disciplineMathematics and Statisticsen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US
dc.contributor.committeeMemberSrinivasan, Rajen_US
dc.contributor.committeeMemberSoteros, Chrisen_US
dc.contributor.committeeMemberMartin, John R.en_US
dc.contributor.committeeMemberKelly, Ivan W.en_US


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