A general family of trimmed estimators for robust high-dimensional data

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dc.contributor.authorYang, Eunhoko
dc.contributor.authorLozano, Aurelie C.ko
dc.contributor.authorAravkin, Aleksandrko
dc.date.accessioned2018-12-20T08:05:59Z-
dc.date.available2018-12-20T08:05:59Z-
dc.date.created2018-11-16-
dc.date.created2018-11-16-
dc.date.created2018-11-16-
dc.date.created2018-11-16-
dc.date.created2018-11-16-
dc.date.issued2018-10-
dc.identifier.citationELECTRONIC JOURNAL OF STATISTICS, v.12, no.2, pp.3519 - 3553-
dc.identifier.issn1935-7524-
dc.identifier.urihttp://hdl.handle.net/10203/248758-
dc.description.abstractWe consider the problem of robustifying high-dimensional structured estimation. Robust techniques are key in real-world applications which often involve outliers and data corruption. We focus on trimmed versions of structurally regularized M-estimators in the high-dimensional setting, including the popular Least Trimmed Squares estimator, as well as analogous estimators for generalized linear models and graphical models, using convex and non-convex loss functions. We present a general analysis of their statistical convergence rates and consistency, and then take a closer look at the trimmed versions of the Lasso and Graphical Lasso estimators as special cases. On the optimization side, we show how to extend algorithms for M-estimators to fit trimmed variants and provide guarantees on their numerical convergence. The generality and competitive performance of high-dimensional trimmed estimators are illustrated numerically on both simulated and real-world genomics data.-
dc.languageEnglish-
dc.publisherINST MATHEMATICAL STATISTICS-
dc.titleA general family of trimmed estimators for robust high-dimensional data-
dc.typeArticle-
dc.identifier.wosid000460450800041-
dc.identifier.scopusid2-s2.0-85063397918-
dc.type.rimsART-
dc.citation.volume12-
dc.citation.issue2-
dc.citation.beginningpage3519-
dc.citation.endingpage3553-
dc.citation.publicationnameELECTRONIC JOURNAL OF STATISTICS-
dc.identifier.doi10.1214/18-EJS1470-
dc.contributor.localauthorYang, Eunho-
dc.contributor.nonIdAuthorLozano, Aurelie C.-
dc.contributor.nonIdAuthorAravkin, Aleksandr-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorLasso-
dc.subject.keywordAuthorrobust estimation-
dc.subject.keywordAuthorhigh-dimensional variable selection-
dc.subject.keywordAuthorsparse learning-
dc.subject.keywordPlusREGRESSION SHRINKAGE-
dc.subject.keywordPlusSELECTION-
dc.subject.keywordPlusRECOVERY-
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