题目：Estimation and variable selection for generalized additive partial linear models

报告人✪：Professor Li Wang(美国佐治亚大学统计系)

【Abstract】A class of generalized additive partial linear models is investigated. We propose the use of polynomial spline smoothing for estimation of nonparametric functions, and derive quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving big system 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 likelihood, which is shown to have an oracle property. Monte Carlo simulations and an analysis of a dataset from Pima Indian diabetes study are presented for illustration.

时间：2013年6月24日（周一）下午16:00开始

地点：数学系致远楼105室