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Improved ridge estimators in a linear regression model

Author

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  • Xu-Qing Liu
  • Feng Gao
  • Zhen-Feng Yu

Abstract

In this paper, the notion of the improved ridge estimator (IRE) is put forward in the linear regression model y = X β + e . The problem arises if augmenting the equation 0 = c ′ α + ε instead of 0 = C α + ϵ to the model. Three special IREs are considered and studied under the mean-squared error criterion and the prediction error sum of squares criterion. The simulations demonstrate that the proposed estimators are effective and recommendable, especially when multicollinearity is severe.

Suggested Citation

  • Xu-Qing Liu & Feng Gao & Zhen-Feng Yu, 2013. "Improved ridge estimators in a linear regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(1), pages 209-220, January.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:209-220
    DOI: 10.1080/02664763.2012.740623
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    Cited by:

    1. R. Salmerón & J. García & C. B. García & M. M. López Martín, 2017. "A note about the corrected VIF," Statistical Papers, Springer, vol. 58(3), pages 929-945, September.
    2. José García‐Pérez & María del Mar López‐Martín & Catalina García‐García & Román Salmerón‐Gómez, 2020. "A Geometrical Interpretation of Collinearity: A Natural Way to Justify Ridge Regression and Its Anomalies," International Statistical Review, International Statistical Institute, vol. 88(3), pages 776-792, December.

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