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Robust And Misspecification Resistant Model Selection In Regression Models With Information Complexity And Genetic Algorithms, Yan Liu

Doctoral Dissertations

In this dissertation, we develop novel computationally effiient model subset selection methods for multiple and multivariate linear regression models which are both robust and misspecification resistant. Our approach is to use a three-way hybrid method which employs the information theoretic measure of complexity (ICOMP) computed on robust M-estimators as model subset selection criteria, integrated with genetic algorithms (GA) as the subset model searching engine.

Despite the rich literature on the robust estimation techniques, bridging the theoretical and applied aspects related to robust model subset selection has been somewhat neglected. A few information criteria in the multiple regression literature are robust. …