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dc.contributor.advisorTang, Cheng Yong
dc.creatorJing, Naimin
dc.date.accessioned2021-08-23T17:56:13Z
dc.date.available2021-08-23T17:56:13Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/20.500.12613/6857
dc.description.abstractModern large data sets inevitably contain outliers that deviate from the model assumptions. However, many widely used estimators, such as maximum likelihood estimators and least squared estimators, perform weakly with the existence of outliers. Alternatively, many statistical modeling approaches have matrices as the parameters. We consider penalized estimators for matrix-valued parameters with a focus on their robustness properties in the presence of outliers. We propose a general framework for robust modeling with matrix-valued parameters by minimizing robust loss functions with penalization. However, there are challenges to this approach in both computation and theoretical analysis. To tackle the computational challenges from the large size of the data, non-smoothness of robust loss functions, and the slow speed of matrix operations, we propose to apply the Frank-Wolfe algorithm, a first-order algorithm for optimization on a restricted region with low computation burden per iteration. Theoretically, we establish finite-sample error bounds under high-dimensional settings. We show that the estimation errors are bounded by small terms and converge in probability to zero under mild conditions in a neighborhood of the true model. Our method accommodates a broad classes of modeling problems using robust loss functions with penalization. Concretely, we study three cases: matrix completion, multivariate regression, and network estimation. For all cases, we illustrate the robustness of the proposed method both theoretically and numerically.
dc.format.extent156 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.subjectFrank-Wolfe algorithm
dc.subjectMatrix-valued parameter
dc.subjectNon-asymptotic properties
dc.subjectNon-smooth criterion function
dc.subjectPenalized regression
dc.subjectRobust estimation
dc.titleRobust Approaches for Matrix-Valued Parameters
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberAiroldi, Edoardo
dc.contributor.committeememberLee, Kuang-Yao
dc.contributor.committeememberFang, Ethan X.
dc.description.departmentStatistics
dc.relation.doihttp://dx.doi.org/10.34944/dspace/6839
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.proqst14621
dc.date.updated2021-08-21T10:08:43Z
refterms.dateFOA2021-08-23T17:56:14Z
dc.identifier.filenameJing_temple_0225E_14621.pdf


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