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Positive Semidefinite Rank-Based Correlation Matrix Estimation With Application to Semiparametric Graph Estimation

Author(s): Zhao, Tuo; Roeder, Kathryn; Liu, Han

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dc.contributor.authorZhao, Tuo-
dc.contributor.authorRoeder, Kathryn-
dc.contributor.authorLiu, Han-
dc.date.accessioned2021-10-11T14:16:53Z-
dc.date.available2021-10-11T14:16:53Z-
dc.date.issued2014en_US
dc.identifier.citationZhao, Tuo, Kathryn Roeder, and Han Liu. "Positive semidefinite rank-based correlation matrix estimation with application to semiparametric graph estimation." Journal of Computational and Graphical Statistics 23, no. 4 (2014): 895-922.en_US
dc.identifier.issn1061-8600-
dc.identifier.urihttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219653/-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr12k33-
dc.description.abstractMany statistical methods gain robustness and flexibility by sacrificing convenient computational structures. In this article, we illustrate this fundamental tradeoff by studying a semiparametric graph estimation problem in high dimensions. We explain how novel computational techniques help to solve this type of problem. In particular, we propose a nonparanormal neighborhood pursuit algorithm to estimate high-dimensional semiparametric graphical models with theoretical guarantees. Moreover, we provide an alternative view to analyze the tradeoff between computational efficiency and statistical error under a smoothing optimization framework. Though this article focuses on the problem of graph estimation, the proposed methodology is widely applicable to other problems with similar structures. We also report thorough experimental results on text, stock, and genomic datasets.en_US
dc.format.extent895 - 922en_US
dc.language.isoen_USen_US
dc.relation.ispartofJournal of Computational and Graphical Statisticsen_US
dc.rightsAuthor's manuscripten_US
dc.titlePositive Semidefinite Rank-Based Correlation Matrix Estimation With Application to Semiparametric Graph Estimationen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1080/10618600.2013.858633-
dc.date.eissued2014-10-20en_US
dc.identifier.eissn1537-2715-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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