JSM 2005 - Toronto

Abstract #304158

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Activity Number: 101
Type: Contributed
Date/Time: Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor: Business and Economics Statistics Section
Abstract - #304158
Title: Robust- and Misspecification-resistant Multivariate Regression Models Hybridized with Genetic Algorithms and Information Complexity
Author(s): Yan Liu*+ and Hamparsum Bozdogan
Companies: University of Tennessee and University of Tennessee
Address: 1611 Laurel Avenue, Apt 808, Knoxville, TN, 37916,
Keywords: Robust Multivariate Regression ; Model Misspecification ; Information Complexity ; Genetic Algorithms ; Subset Selection
Abstract:

In this paper, we extend our idea of robust- and misspecification-resistant multiple regression model selection to its multivariate regression analogue. In robust multivariate regression, we develop Bozdogan's information theoretic measure of complexity (ICOMP) criterion along with its misspecification part in the covariance penalty via the idea of entropic complexity measure. This approach unifies robustness and the model misspecification in the multivariate regression case in one criterion function for model selection. As a result, ICOMP takes into account robustness, misspecification, the presence of outliers, autocorrelation, and the heteroscedasticity in the model. Genetic algorithms (GA) are used to select the optimal subset of variables. The genetic algorithms enable the completion of the computation in a reasonable amount of time. We demonstrate the flexibility of our three-way hybrid method with GA on several benchmark datasets and show it is feasible to dynamically choose the optimal fitting models with high efficiency.

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