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dc.contributor.authorYan, Ying
dc.date.accessioned2010-09-30 19:17:13 (GMT)
dc.date.available2010-09-30 19:17:13 (GMT)
dc.date.issued2010-09-30T19:17:13Z
dc.date.submitted2010
dc.identifier.urihttp://hdl.handle.net/10012/5558
dc.description.abstractIn this thesis, we are interested in empirical likelihood (EL) methods for two-sample problems, with focus on the difference of the two population means. A weighted empirical likelihood method (WEL) for two-sample problems is developed. We also consider a scenario where sample data on auxiliary variables are fully observed for both samples but values of the response variable are subject to missingness. We develop an adjusted empirical likelihood method for inference of the difference of the two population means for this scenario where missing values are handled by a regression imputation method. Bootstrap calibration for WEL is also developed. Simulation studies are conducted to evaluate the performance of naive EL, WEL and WEL with bootstrap calibration (BWEL) with comparison to the usual two-sample t-test in terms of power of the tests and coverage accuracies. Simulation for the adjusted EL for the linear regression model with missing data is also conducted.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectempirical likelihooden
dc.subjecttwo sampleen
dc.titleEmpirical Likelihood Inference for Two-Sample Problemsen
dc.typeMaster Thesisen
dc.comment.hiddenCould you please review my master's thesis by the afternoon of Sept. 30? I missunderstood the guideline and don't submit this pdf until today, though I have done my thesis' work in the early August. It is my deadline, Thanks!en
dc.pendingfalseen
dc.subject.programStatisticsen
uws-etd.degree.departmentStatistics and Actuarial Scienceen
uws-etd.degreeMaster of Mathematicsen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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