JSM 2005 Online Program

Abstract #302417

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. 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, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	114
Type:	Invited
Date/Time:	Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor:	IMS
Abstract - #302417
Title:	Aggregation for Regression Learning
Author(s):	Marten H. Wegkamp*+ and Florentina Bunea and Alexandre B. Tsybakov
Companies:	Florida State University and Florida State University and University Paris VI
Address:	Department of Statistics, Tallahassee, FL, 32306-4330, United States
Keywords:	aggregation ; minimax risk ; model selection ; nonparametric regression ; oracle inequality
Abstract:	In this paper, we explore aggregation of arbitrary estimators in regression models. A motivating factor is the existence of many methods of estimation, leading to possibly competing procedures. Local polynomial kernel smoothing, penalized least squares or likelihood, and spline or wavelet estimators are classes of methods that represent major trends in nonparametric estimation of regression. When no method is a clear winner, one may prefer to combine different estimators obtained via different methods. Furthermore, within each method, one can obtain competing estimators for different values of the smoothing parameter (the bandwidth in kernel procedures, the calibrating constant in the penalty term, the threshold value for wavelet estimation, etc.).

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