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dc.contributor.advisorDong, Yuexiao
dc.creatorYang, Chaozheng
dc.date.accessioned2020-11-05T16:15:49Z
dc.date.available2020-11-05T16:15:49Z
dc.date.issued2016
dc.identifier.other965642668
dc.identifier.urihttp://hdl.handle.net/20.500.12613/3880
dc.description.abstractThis dissertation focuses on two problems in dimension reduction. One is using permutation approach to test predictor contribution. The permutation approach applies to marginal coordinate tests based on dimension reduction methods such as SIR, SAVE and DR. This approach no longer requires calculation of the method-specific weights to determine the asymptotic null distribution. The other one is through combining clustering method with robust regression (least absolute deviation) to estimate dimension reduction subspace. Compared with ordinary least squares, the proposed method is more robust to outliers; also, this method replaces the global linearity assumption with the more flexible local linearity assumption through k-means clustering.
dc.format.extent77 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.subjectEstimate Dimension Reduction Subspace
dc.subjectSufficient Dimension Reduction
dc.subjectTest Predictor Contribution
dc.titleSufficient Dimension Reduction in Complex Datasets
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberWei, William W. S.
dc.contributor.committeememberTang, Cheng-Yong
dc.contributor.committeememberDing, Shanshan
dc.description.departmentStatistics
dc.relation.doihttp://dx.doi.org/10.34944/dspace/3862
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
refterms.dateFOA2020-11-05T16:15:49Z


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