Please use this identifier to cite or link to this item: https://doi.org/10.1080/10485252.2013.797977
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dc.titleResampling-based efficient shrinkage method for non-smooth minimands
dc.contributor.authorXu, J.
dc.date.accessioned2014-10-28T05:14:43Z
dc.date.available2014-10-28T05:14:43Z
dc.date.issued2013-09
dc.identifier.citationXu, J. (2013-09). Resampling-based efficient shrinkage method for non-smooth minimands. Journal of Nonparametric Statistics 25 (3) : 731-743. ScholarBank@NUS Repository. https://doi.org/10.1080/10485252.2013.797977
dc.identifier.issn10485252
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105332
dc.description.abstractIn 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. © 2013 Copyright American Statistical Association and Taylor & Francis.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1080/10485252.2013.797977
dc.sourceScopus
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.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1080/10485252.2013.797977
dc.description.sourcetitleJournal of Nonparametric Statistics
dc.description.volume25
dc.description.issue3
dc.description.page731-743
dc.identifier.isiut000322617000012
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