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Shrinkage ridge regression in partial linear models

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  • Mahdi Roozbeh
  • Mohammad Arashi

Abstract

In this paper, shrinkage ridge estimator and its positive part are defined for the regression coefficient vector in a partial linear model. The differencing approach is used to enjoy the ease of parameter estimation after removing the non parametric part of the model. The exact risk expressions in addition to biases are derived for the estimators under study and the region of optimality of each estimator is exactly determined. The performance of the estimators is evaluated by simulated as well as real data sets.

Suggested Citation

  • Mahdi Roozbeh & Mohammad Arashi, 2016. "Shrinkage ridge regression in partial linear models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(20), pages 6022-6044, October.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:20:p:6022-6044
    DOI: 10.1080/03610926.2014.955115
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    Cited by:

    1. Chien-Chia L. Huang & Yow-Jen Jou & Hsun-Jung Cho, 2017. "Difference-based matrix perturbation method for semi-parametric regression with multicollinearity," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2161-2171, September.
    2. Bahadır Yüzbaşı & Mohammad Arashi & S. Ejaz Ahmed, 2020. "Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High‐Dimension Regime," International Statistical Review, International Statistical Institute, vol. 88(1), pages 229-251, April.

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