title: ESTIMATION AND VARIABLE SELECTION FOR GENERALIZED ADDITIVE PARTIAL LINEAR MODELS.
abstract: We 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.
authors.affiliation: Department of Statistics, University of Georgia, Athens, Georgia 30602, USA,  lilywang@uga.edu.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.forename: Xiang
authors.initials: X
authors.lastname: Liu
authors.forename: Hua
authors.initials: H
authors.lastname: Liang
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Carroll