DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yang, Eunho | ko |
dc.contributor.author | Lozano, Aurelie C. | ko |
dc.contributor.author | Aravkin, Aleksandr | ko |
dc.date.accessioned | 2018-12-20T08:05:59Z | - |
dc.date.available | 2018-12-20T08:05:59Z | - |
dc.date.created | 2018-11-16 | - |
dc.date.created | 2018-11-16 | - |
dc.date.created | 2018-11-16 | - |
dc.date.created | 2018-11-16 | - |
dc.date.created | 2018-11-16 | - |
dc.date.issued | 2018-10 | - |
dc.identifier.citation | ELECTRONIC JOURNAL OF STATISTICS, v.12, no.2, pp.3519 - 3553 | - |
dc.identifier.issn | 1935-7524 | - |
dc.identifier.uri | http://hdl.handle.net/10203/248758 | - |
dc.description.abstract | We 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.language | English | - |
dc.publisher | INST MATHEMATICAL STATISTICS | - |
dc.title | A general family of trimmed estimators for robust high-dimensional data | - |
dc.type | Article | - |
dc.identifier.wosid | 000460450800041 | - |
dc.identifier.scopusid | 2-s2.0-85063397918 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 3519 | - |
dc.citation.endingpage | 3553 | - |
dc.citation.publicationname | ELECTRONIC JOURNAL OF STATISTICS | - |
dc.identifier.doi | 10.1214/18-EJS1470 | - |
dc.contributor.localauthor | Yang, Eunho | - |
dc.contributor.nonIdAuthor | Lozano, Aurelie C. | - |
dc.contributor.nonIdAuthor | Aravkin, Aleksandr | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Lasso | - |
dc.subject.keywordAuthor | robust estimation | - |
dc.subject.keywordAuthor | high-dimensional variable selection | - |
dc.subject.keywordAuthor | sparse learning | - |
dc.subject.keywordPlus | REGRESSION SHRINKAGE | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | RECOVERY | - |
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