Statistics and Data Science Seminar
Li Wang
University of Georgia
Estimation and Variable Selection for Generalized Additive Partial Linear Models
Abstract: We study a class of generalized additive partial linear
models. We propose the use of polynomial spline smoothing for estimation
of nonparametric functions, and derive the 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.
Tuesday November 23, 2010 at 1:30 PM in SEO 636