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dc.contributor.authorWang, Li
dc.contributor.authorLiu, Xiang
dc.contributor.authorLiang, Hua
dc.contributor.authorCarroll, Raymond J.
dc.date.accessioned2016-02-25T13:17:08Z
dc.date.available2016-02-25T13:17:08Z
dc.date.issued2011-08
dc.identifier.citationWang L, Liu X, Liang H, Carroll RJ (2011) Estimation and variable selection for generalized additive partial linear models. The Annals of Statistics 39: 1827–1851. Available: http://dx.doi.org/10.1214/11-AOS885.
dc.identifier.issn0090-5364
dc.identifier.doi10.1214/11-AOS885
dc.identifier.urihttp://hdl.handle.net/10754/598236
dc.description.abstractWe study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.
dc.description.sponsorshipSupported by NSF Grant DMS-09-05730.Supported by a Merck Quantitative Sciences Fellowship Program.Supported by a grant from the National Cancer Institute (CA57030) and by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).
dc.publisherInstitute of Mathematical Statistics
dc.subjectBackfitting
dc.subjectGeneralized additive models
dc.subjectGeneralized partially linear models
dc.subjectLASSO
dc.subjectNonconcave penalized likelihood
dc.subjectPenalty-based variable selection
dc.subjectPolynomial spline
dc.subjectQuasi-likelihood
dc.subjectSCAD
dc.subjectShrinkage methods.
dc.titleEstimation and variable selection for generalized additive partial linear models
dc.typeArticle
dc.identifier.journalThe Annals of Statistics
dc.contributor.institutionThe University of Georgia, Athens, United States
dc.contributor.institutionTexas A and M University, College Station, United States
dc.contributor.institutionUniversity of Rochester, Rochester, United States
kaust.grant.numberKUS-CI-016-04


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