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Sparse optimization for nonconvex group penalized estimation

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dc.contributor.authorSangin Lee
dc.contributor.authorOh, Miae
dc.contributor.authorYongdai Kim
dc.date.accessioned2017-01-25T04:08:27Z
dc.date.available2017-01-25T04:08:27Z
dc.date.issued2015-03-30
dc.identifier.issn0094-9655
dc.identifier.urihttps://repository.kihasa.re.kr/handle/201002/24943
dc.description.abstractWe consider a linear regression model where there are group structures in covariates. The group LASSO has been proposed for group variable selections. Many nonconvex penalties such as smoothly clipped absolute deviation and minimax concave penalty were extended to group variable selection problems. The group coordinate descent (GCD) algorithm is used popularly for fitting these models. However, the GCD algorithms are hard to be applied to nonconvex group penalties due to computational complexity unless the design matrix is orthogonal. In this paper, we propose an efficient optimization algorithm for nonconvex group penalties by combining the concave convex procedure and the group LASSO algorithm. We also extend the proposed algorithm for generalized linear models. We evaluate numerical efficiency of the proposed algorithm compared to existing GCD algorithms through simulated data and real data sets.
dc.format.extent14
dc.languageeng
dc.publisherTaylor & Francis
dc.titleSparse optimization for nonconvex group penalized estimation
dc.typeArticle
dc.type.localArticle(Academic)
dc.identifier.apprname학술논문평가
dc.subject.keywordconcave convex procedure
dc.subject.keywordgroup LASSO
dc.subject.keywordnonconvex penalty
dc.subject.keywordvariable selection
dc.contributor.affiliatedAuthor오미애
dc.citation.titleJournal of Statistical Computation and Simulation
dc.citation.volume86
dc.citation.number3
dc.citation.startPage597
dc.citation.endPage610
dc.identifier.bibliographicCitationJournal of Statistical Computation and Simulation, vol. 86, no. 3, pp. 597 - 610
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