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Article: Resampling-based efficient shrinkage method for non-smooth minimands

TitleResampling-based efficient shrinkage method for non-smooth minimands
Authors
KeywordsAccelerated Failure Time Model
Adaptive Lasso
Lars
Lasso
Maximum Rank Correlation
Quantile Regression
Resampling
Variable Selection
Issue Date2013
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10485252.asp
Citation
Journal of Nonparametric Statistics, 2013, v. 25, p. 731-743 How to Cite?
AbstractJournal of the American Statistical Association In many regression models, the coefficients are typically estimated by optimising an objective function with a U-statistic structure. Under such a setting, we propose a simple and general method for simultaneous coefficient estimation and variable selection. It combines an efficient quadratic approximation of the objective function with the adaptive lasso penalty to yield a piecewise-linear regularisation path which can be easily obtained from the fast lars–lasso algorithm. Furthermore, the standard asymptotic oracle properties can be established under general conditions without requiring the covariance assumption (Wang, H., and Leng, C. (2007), ‘Unified Lasso Estimation by Least Squares Approximation’, Journal of the American Statistical Association , 102, 1039–1048). This approach applies to many semiparametric regression problems. Three examples are used to illustrate the practical utility of our proposal. Numerical results based on simulated and real data are provided.
Persistent Identifierhttp://hdl.handle.net/10722/221671
ISSN
2015 Impact Factor: 0.446
2015 SCImago Journal Rankings: 0.980
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, J-
dc.date.accessioned2015-12-04T15:28:59Z-
dc.date.available2015-12-04T15:28:59Z-
dc.date.issued2013-
dc.identifier.citationJournal of Nonparametric Statistics, 2013, v. 25, p. 731-743-
dc.identifier.issn1048-5252-
dc.identifier.urihttp://hdl.handle.net/10722/221671-
dc.description.abstractJournal of the American Statistical Association In many regression models, the coefficients are typically estimated by optimising an objective function with a U-statistic structure. Under such a setting, we propose a simple and general method for simultaneous coefficient estimation and variable selection. It combines an efficient quadratic approximation of the objective function with the adaptive lasso penalty to yield a piecewise-linear regularisation path which can be easily obtained from the fast lars–lasso algorithm. Furthermore, the standard asymptotic oracle properties can be established under general conditions without requiring the covariance assumption (Wang, H., and Leng, C. (2007), ‘Unified Lasso Estimation by Least Squares Approximation’, Journal of the American Statistical Association , 102, 1039–1048). This approach applies to many semiparametric regression problems. Three examples are used to illustrate the practical utility of our proposal. Numerical results based on simulated and real data are provided.-
dc.languageeng-
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10485252.asp-
dc.relation.ispartofJournal of Nonparametric Statistics-
dc.subjectAccelerated Failure Time Model-
dc.subjectAdaptive Lasso-
dc.subjectLars-
dc.subjectLasso-
dc.subjectMaximum Rank Correlation-
dc.subjectQuantile Regression-
dc.subjectResampling-
dc.subjectVariable Selection-
dc.titleResampling-based efficient shrinkage method for non-smooth minimands-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1080/10485252.2013.797977-
dc.identifier.scopuseid_2-s2.0-84881662936-
dc.identifier.volume25-
dc.identifier.spage731-
dc.identifier.epage743-
dc.identifier.isiWOS:000322617000012-

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