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dc.contributor.advisorSun, Jianguo, 1961-eng
dc.contributor.authorZhang, Bin, 1979-eng
dc.date.issued2009eng
dc.date.submitted2009 Summereng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on September 17, 2010).eng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.eng
dc.descriptionDissertation advisor: Dr. (Tony) Jianguo Sun.eng
dc.descriptionVita.eng
dc.descriptionPh. D. University of Missouri--Columbia 2009.eng
dc.description.abstractAs a branch of statistics, survival analysis, which is often referred to as "reliability theory" in engineering, has a long history. While in practical problems, some information might be missing. This dissertation discusses two types of missing information. The first type is caused by the censoring scheme, for example, the current status data. Several semiparametric models, e.g. the transformation models and the proportional odds models, are applied to the univariate and bivariate current status data. Efficient estimates are derived and the large sample properties of the estimates are provided. The other type is caused by sampling structure. It happens when the individuals do not have the same probabilities to be selected, for example, the stratified sampling. A biased sample problem is considered with the parameter of interest defined by some unbiased estimating equations. For the analysis, empirical likelihood approach is used and the likelihood ratio statistic is proved to follow a chi-square distribution asymptotically. Simulation studies and real data analysis are conducted to indicate that the presented approaches perform well in practical problems.eng
dc.description.bibrefIncludes bibliographical referenceseng
dc.format.extentx, 105 pageseng
dc.identifier.oclc697297946eng
dc.identifier.urihttps://hdl.handle.net/10355/9664
dc.identifier.urihttps://doi.org/10.32469/10355/9664eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.lcshSurvival analysis (Biometry)eng
dc.subject.lcshMissing observations (Statistics)eng
dc.subject.lcshStatistical hypothesis testingeng
dc.titleStatistical analysis for survival data with missing informationeng
dc.typeThesiseng
thesis.degree.disciplineStatistics (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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