JSM 2004 - Toronto

Abstract #300222

This is the preliminary program for the 2004 Joint Statistical Meetings in Toronto, Canada. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2004); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.


Back to main JSM 2004 Program page



Activity Number: 364
Type: Invited
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #300222
Title: Variable Selection via COSSO in Nonparametric Regression
Author(s): Yi Lin and Hao H. Zhang*+
Companies: University of Wisconsin, Madison and North Carolina State University
Address: 8203 Campus Box, Raleigh, NC, 27695-8203,
Keywords: smoothing spline ANOVA ; variable selection ; lasso ; shrinkage ; nonparametric ; soft thresholding
Abstract:

We propose a new method for model selection and model fitting in nonparametric regression models, in the framework of smoothing spline ANOVA. The "COSSO'' is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed in the traditional smoothing spline method. The COSSO provides a unified framework for several recent proposals for model selection in linear models and smoothing spline ANOVA models. Theoretical properties, such as the existence and the rate of convergence of the COSSO estimator, are studied. In the special case of a tensor product design with periodic functions, a detailed analysis reveals that the COSSO applies a novel soft-thresholding-type operation to the function components and selects the correct model structure with probability tending to one. We give an equivalent formulation of the COSSO estimator which leads naturally to an iterative algorithm. A novel one-step update algorithm is proposed, which is computationally fast and efficient. Simulations and real examples show that the COSSO gives very competitive performances when compared with other methods.


  • 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 2004 program

JSM 2004 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.
Revised March 2004