{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T10:19:51Z","timestamp":1758709191310},"reference-count":60,"publisher":"Cambridge University Press (CUP)","issue":"6","license":[{"start":{"date-parts":[[2013,8,7]],"date-time":"2013-08-07T00:00:00Z","timestamp":1375833600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2013,12]]},"abstract":"<jats:p>This paper studies generalized additive partial linear models with high-dimensional covariates. We are interested in which components (including parametric and nonparametric components) are nonzero. The additive nonparametric functions are approximated by polynomial splines. We propose a doubly penalized procedure to obtain an initial estimate and then use the adaptive least absolute shrinkage and selection operator to identify nonzero components and to obtain the final selection and estimation results. We establish selection and estimation consistency of the estimator in addition to asymptotic normality for the estimator of the parametric components by employing a penalized quasi-likelihood. Thus our estimator is shown to have an asymptotic oracle property. Monte Carlo simulations show that the proposed procedure works well with moderate sample sizes.<\/jats:p>","DOI":"10.1017\/s0266466613000029","type":"journal-article","created":{"date-parts":[[2013,8,7]],"date-time":"2013-08-07T09:46:45Z","timestamp":1375868805000},"page":"1136-1161","source":"Crossref","is-referenced-by-count":10,"title":["GENERALIZED ADDITIVE PARTIAL LINEAR MODELS WITH HIGH-DIMENSIONAL COVARIATES"],"prefix":"10.1017","volume":"29","author":[{"given":"Heng","family":"Lian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2013,8,7]]},"reference":[{"key":"S0266466613000029_ref46","volume-title":"Applications of Empirical Process Theory","author":"van der Geer","year":"2000"},{"key":"S0266466613000029_ref54","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511615887","volume-title":"Semiparametric Regression for the Applied Econometrician","author":"Yatchew","year":"2003"},{"key":"S0266466613000029_ref4","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1214\/08-AOS620","article-title":"Simultaneous analysis of LASSO and Dantzig selector","volume":"37","author":"Bickel","year":"2009","journal-title":"Annals of Statistics"},{"key":"S0266466613000029_ref41","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1111\/j.2517-6161.1988.tb01738.x","article-title":"Kernel smoothing in partial linear models","volume":"50","author":"Speckman","year":"1988","journal-title":"Journal of the Royal Statistical Society, Series B"},{"key":"S0266466613000029_ref57","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1214\/07-AOS520","article-title":"The sparsity and bias of the lasso selection in high-dimensional linear regression","volume":"36","author":"Zhang","year":"2008","journal-title":"Annals of Statistics"},{"key":"S0266466613000029_ref13","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1214\/009053604000000256","article-title":"Nonconcave penalized likelihood with a diverging number of parameters","volume":"32","author":"Fan","year":"2004","journal-title":"Annals of Statistics"},{"key":"S0266466613000029_ref35","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S0167-9473(98)00033-4","article-title":"Direct generalized additive modeling with penalized likelihood","volume":"28","author":"Marx","year":"1998","journal-title":"Computational Statistics and Data Analysis"},{"key":"S0266466613000029_ref5","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1214\/aos\/1176347115","article-title":"Linear smoothers and additive models","volume":"17","author":"Buja","year":"1989","journal-title":"Annals of Statistics"},{"key":"S0266466613000029_ref33","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1093\/biomet\/82.1.93","article-title":"A kernel method of estimating structured nonparametric regression based on marginal integration","volume":"82","author":"Linton","year":"1995","journal-title":"Biometrika"},{"key":"S0266466613000029_ref38","doi-asserted-by":"crossref","first-page":"931","DOI":"10.2307\/1912705","article-title":"Root n-consistent semiparametric regression","volume":"56","author":"Robinson","year":"1988","journal-title":"Econometrica"},{"key":"S0266466613000029_ref17","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1111\/j.2517-6161.1986.tb01407.x","article-title":"Spline smoothing in partly linear models","volume":"48","author":"Heckman","year":"1986","journal-title":"Journal of the Royal Statistical Society, Series B"},{"key":"S0266466613000029_ref60","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1214\/009053607000000802","article-title":"One-step sparse estimates in nonconcave penalized likelihood models","volume":"36","author":"Zou","year":"2008","journal-title":"Annals of Statistics"},{"key":"S0266466613000029_ref58","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1198\/jasa.2011.tm10281","article-title":"Linear or nonlinear? 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