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dc.contributor.advisorDong, Yuexiao
dc.creatorAlkan, Gunes
dc.date.accessioned2021-08-23T17:59:39Z
dc.date.available2021-08-23T17:59:39Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/20.500.12613/6872
dc.description.abstractIn recent years, the need for models which can accommodate higher order covariates have increased greatly. We first consider linear regression with vector-valued response Y and tensor-valued predictors X. Envelope models (Cook et al., 2010) can significantly improve the estimation efficiency of the regression coefficients by linking the regression mean with the covariance of the regression error. Most existing tensor regression models assume that the conditional distribution of Y given X follows a normal distribution, which may be violated in practice. In Chapter 2, we propose an envelope multivariate linear regression model with tensor-valued predictors and elliptically contoured error distributions. The proposed estimator is more robust to violations of the error normality assumption, and it is more efficient than the estimators without considering the underlying envelope structure. We compare the new proposal with existing estimators in extensive simulation studies. In Chapter 3, we explore how the missing data problem can be addressed for multivariate linear regression setting with envelopes and elliptical error. A popular and efficient approach, multiple imputation is implemented with bootstrapped expectation-maximization (EM) algorithm to fill the missing data, which is then followed with an adjustment in estimating regression coefficients. Simulations with synthetic data as well as real data are presented to establish the superiority of the adjusted multiple imputation method proposed.
dc.format.extent70 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectStatistics
dc.subjectElliptical distributions
dc.subjectEnvelope model
dc.subjectTensor
dc.titleENVELOPE MODEL FOR MULTIVARIATE LINEAR REGRESSION WITH ELLIPTICAL ERROR
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberChitturi, Pallavi
dc.contributor.committeememberLee, Kuang-Yao
dc.contributor.committeememberShen, Cencheng
dc.description.departmentStatistics
dc.relation.doihttp://dx.doi.org/10.34944/dspace/6854
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
dc.identifier.proqst14626
dc.creator.orcid0000-0001-9356-2173
dc.date.updated2021-08-21T10:08:54Z
refterms.dateFOA2021-08-23T17:59:40Z
dc.identifier.filenameAlkan_temple_0225E_14626.pdf


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