JSM 2004 - Toronto

Abstract #301953

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: 376
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #301953
Title: Robust and Misspecification Resistant Model Selection with Information Complexity and Genetic Algorithms
Author(s): Yan Liu*+ and Hamparsum Bozdogan
Companies: University of Tennessee and University of Tennessee
Address: 331 Stokely Management Center, Knoxville, TN, 37996-0532,
Keywords: robust regression ; misspecification ; information complexity ; genetic algorithms ; subset selection
Abstract:

We introduce and develop a new unified theory of model selection, which is robust and at the same time misspecification-resistant based on Bozdogan's information-theoretic measure of complexity (ICOMP) criterion. In developing ICOMP in robust regression, we take into account several critical issues, which include the model misspecification, the presence of outliers and the existence of autocorrelation and heteroscedasticity. With ICOMP in robust regression, we use the genetic algorithms (GA) to select the optimal subset of variables. The genetic algorithms enable the rapid computation of model subset selections that would otherwise be impossible in a reasonable amount of time. As a result, it is now feasible to automatically and dynamically develop the best-fitting models with many different combinations of variables.

  • 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