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Ridge regression in two‐parameter solution

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  • Stan Lipovetsky
  • W. Michael Conklin

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 outperforms 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 convenient for the analysis and interpretation of the regression. Numerical runs prove that this technique works very well. The examples are considered for marketing research problems. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Stan Lipovetsky & W. Michael Conklin, 2005. "Ridge regression in two‐parameter solution," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(6), pages 525-540, November.
    • Handle: RePEc:wly:apsmbi:v:21:y:2005:i:6:p:525-540
    DOI: 10.1002/asmb.603
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