Asymptotic oracle properties of SCAD-penalized least squares estimators

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dc.creatorHuang, Jian
dc.creatorXie, Huiliang
dc.date2007-09-06
dc.date.accessioned2026-07-07T08:28:40Z
dc.date.available2026-07-07T08:28:40Z
dc.descriptionWe study the asymptotic properties of the SCAD-penalized least squares estimator in sparse, high-dimensional, linear regression models when the number of covariates may increase with the sample size. We are particularly interested in the use of this estimator for simultaneous variable selection and estimation. We show that under appropriate conditions, the SCAD-penalized least squares estimator is consistent for variable selection and that the estimators of nonzero coefficients have the same asymptotic distribution as they would have if the zero coefficients were known in advance. Simulation studies indicate that this estimator performs well in terms of variable selection and estimation.
dc.descriptionPublished at http://dx.doi.org/10.1214/074921707000000337 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0709.0863
dc.identifierhttp://arxiv.org/abs/0709.0863
dc.identifierIMS Lecture Notes Monograph Series 2007, Vol. 55, 149-166
dc.identifierdoi:10.1214/074921707000000337
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/137698
dc.subjectStatistics Theory
dc.subject62J07 (Primary) 62E20 (Secondary)
dc.titleAsymptotic oracle properties of SCAD-penalized least squares estimators
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