dc.contributor.authorLian, Heng
dc.contributor.authorLiang, Hua
dc.identifier.citationLian, H., & Liang, H. (2013). Generalized additive partial linear models with high-dimensional covariates. Econometric theory, 29(6), 1136-1161.en_US
dc.description.abstractThis 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.en_US
dc.relation.ispartofseriesEconometric theoryen_US
dc.rights© 2013 Cambridge University Press. This paper was published in Econometric Theory and is made available as an electronic reprint (preprint) with permission of Cambridge University Press. The paper can be found at the following official DOI: [http://dx.doi.org/10.1017/S0266466613000029]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en_US
dc.titleGeneralized additive partial linear models with high-dimensional covariatesen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.versionPublished versionen_US

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