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Restricted ridge estimation


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  • Groß, Jürgen
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    In this paper, we introduce a ridge estimator for the vector of parameters in a linear regression model when additional linear restrictions on the parameter vector are assumed to hold. The estimator is a generalization of the well-known restricted least-squares estimator and is confined to the (affine) subspace which is generated by the restrictions. Necessary and sufficient conditions for the superiority of the new estimator over the restricted least-squares estimator are derived. Our new estimator is not to be confounded with the restricted ridge regression estimator introduced by Sarkar (Comm. Statist. Theory Methods 21 (1992) 1987).

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 65 (2003)
    Issue (Month): 1 (October)
    Pages: 57-64

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    Handle: RePEc:eee:stapro:v:65:y:2003:i:1:p:57-64

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    Keywords: Least squares Linear restrictions Matrix risk Ridge estimator Shrinkage;


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    Cited by:
    1. Jahufer, Aboobacker & Jianbao, Chen, 2009. "Assessing global influential observations in modified ridge regression," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 513-518, February.
    2. Hu Yang & Jianwen Xu, 2011. "Preliminary test Liu estimators based on the conflicting W, LR and LM tests in a regression model with multivariate Student-t error," Metrika, Springer, vol. 73(3), pages 275-292, May.
    3. Roozbeh, M. & Arashi, M., 2013. "Feasible ridge estimator in partially linear models," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 35-44.
    4. Yalian Li & Hu Yang, 2010. "A new stochastic mixed ridge estimator in linear regression model," Statistical Papers, Springer, vol. 51(2), pages 315-323, June.
    5. Özkale, M. Revan, 2009. "A stochastic restricted ridge regression estimator," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1706-1716, September.
    6. M. Alkhamisi, 2010. "Simulation study of new estimators combining the SUR ridge regression and the restricted least squares methodologies," Statistical Papers, Springer, vol. 51(3), pages 651-672, September.
    7. Candelon Bertrand & Hurlin Christophe & Tokpavi Sessi, 2011. "Sampling Error and Double Shrinkage Estimation of Minimum Variance Portfolios," Research Memorandum 002, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).


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