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Recent results in ridge regression methods

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  • M. Alkhamisi
  • I. MacNeill

Abstract

Necessary and sufficient conditions for superiority of the restricted ridge estimator over the restricted least squares estimator are derived when the set of a prior restrictions on parameters are assumed to be incorrect (as well as when the restrictions are assumed to hold). Condition number and trace of mean square error criteria are used to gauge the goodness of some new and some known ridge parameters in rectifying the collinearity problem in three well known real life data sets and a Monte Carlo simulation. Tables 1, 2, 3, 4 and 5 reveal the superiority of the newly formed ridge parameters over some known ridge parameters by means of the foregoing criteria. Copyright Sapienza Università di Roma 2015

Suggested Citation

  • M. Alkhamisi & I. MacNeill, 2015. "Recent results in ridge regression methods," METRON, Springer;Sapienza Università di Roma, vol. 73(3), pages 359-376, December.
    • Handle: RePEc:spr:metron:v:73:y:2015:i:3:p:359-376
    DOI: 10.1007/s40300-015-0065-4
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    References listed on IDEAS

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    1. Groß, Jürgen, 2003. "Restricted ridge estimation," Statistics & Probability Letters, Elsevier, vol. 65(1), pages 57-64, October.
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    Cited by:

    1. Román Salmerón & José García & Catalina García & María del Mar López, 2018. "Transformation of variables and the condition number in ridge estimation," Computational Statistics, Springer, vol. 33(3), pages 1497-1524, September.
    2. Jiefang Dong & Chun Deng & Rongrong Li & Jieyu Huang, 2016. "Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models," Sustainability, MDPI, vol. 9(1), pages 1-15, December.

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