JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 29
Type: Contributed
Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #305674
Title: Model Selection and Estimation in Generalized Additive Models
Author(s): Dong Wang*+ and Daowen Zhang
Companies: North Carolina State University and North Carolina State University
Address: Department of Statistics, Raleigh, NC, 27695-8203, United States
Keywords: Adaptive LASSO ; EM Algorithm ; Smoothing Spline Estimators ; Variable Selection ; Nonparametric Function

A method of model selection and estimation in generalized additive models (GAMs) is proposed. The generalized linear mixed model representation is constructed by utilizing the linear mixed model representation of the smoothing spline estimators of the nonparametric functions, where the importance of the functions is controlled by treating the inverse of the smoothing parameters as extra variance components. By maximizing the penalized quasi-likelihood with the adaptive LASSO, we could effectively select the important nonparametric functions. A unified EM algorithm is also provided to obtain both the maximum likelihood and maximum penalized likelihood estimate of the variance components induced from nonparametric functions. In addition, we estimate the selected functions by the best linear unbiased predictors (BLUPs).

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.