Sparsity-encouraging Data-driven Priors in High-dimensional Problems.

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dc.contributor.advisorRyan Martin, Chair
dc.contributor.advisorAna-Maria Staicu, Member
dc.contributor.advisorSujit Ghosh, Member
dc.contributor.advisorJonathan Williams, Member
dc.contributor.advisorAmanda Lietz, Graduate School Representative
dc.contributor.authorTang, Yiqi
dc.date.accepted2023-09-25
dc.date.accessioned2023-10-31T12:30:25Z
dc.date.available2023-10-31T12:30:25Z
dc.date.defense2023-07-18
dc.date.issued2023-07-18
dc.date.released2023-10-31
dc.date.reviewed2023-08-03
dc.date.submitted2023-07-31
dc.degree.disciplineStatistics
dc.degree.leveldissertation
dc.degree.nameDoctor of Philosophy
dc.descriptionNorth Carolina State University Theses Statistics.
dc.formatPh.D. North Carolina State University, 2023.
dc.identifier.otherdeg34843
dc.identifier.urihttps://www.lib.ncsu.edu/resolver/1840.20/41355
dc.titleSparsity-encouraging Data-driven Priors in High-dimensional Problems.
dcterms.extent1 online resource (ix, 88 pages) : illustrations

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