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