JSM 2004 - Toronto

Abstract #300379

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Activity Number: 116
Type: Contributed
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: General Methodology
Abstract - #300379
Title: Multiobjective Modeling and Two-parameter Ridge Regression
Author(s): Stan Lipovetsky*+ and W. Michael Conklin
Companies: GfK Custom Research Inc. and GfK Custom Research Inc.
Address: 8401 Golden Valley Rd., Minneapolis, MN, 55427,
Keywords: multiple regression ; ridge regression ; multicollinearity ; net effects ; multiobjective modeling ; robustness
Abstract:

We consider simultaneous minimization of the model errors, deviations from orthogonality between regressors and errors, and deviations from other desired properties of the solution. This approach corresponds to a regularized objective that produces a consistent solution not prone to multicollinearity. We obtain a generalization of the ridge regression to two-parameter model that always overperforms a regular one-parameter ridge by better approximation, and has good properties of orthogonality between residuals and predicted values of the dependent variable. The results are very robust and convenient for the analysis of regression. Numerical applications in marketing research demonstrate that this technique works well and is useful for interpretation of the results of regression modeling.

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