JSM 2005 - Toronto

Abstract #302727

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 112
Type: Invited
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #302727
Title: Model Assessment
Author(s): Xiaotong Shen*+ and Hsin-Cheng Huang
Companies: University of Minnesota and Institute of Statistical Science, Academia Sinica
Address: 381 Ford Hall, Minneapolis, MN, 55455, United States
Keywords: Model selection ; Model averaging
Abstract:

In this talk, I will discuss a number of issues involved in model assessment, selection, and averaging from a prediction viewpoint. A general technique of model assessment will be presented based on data perturbation, yielding optimal selection---particularly model selection and combination. From a frequentist perspective, model combination over a selected subset of modeling procedures is attractive, as it controls bias while reducing variability and yields better performance in terms of the accuracy of estimation classification and prediction. To realize the potential of model combination, I will present methodologies for estimating the optimal tuning parameter, such as weights, and subsets for combining via data perturbation. Numerical examples are presented to illustrate main aspects.

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Revised March 2005