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Robust ridge estimator in censored semiparametric linear models

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  • Hadi Emami
  • Korosh Arzideh

Abstract

Besides censoring, multicollinearity and outliers are two common problem in regression analysis. In this paper we propose a family of robust ridge estimators for the censored semiparametric regression models. The proposed robust estimators is based on least trimmed squares (LTS) method. This method is insensitive to corruption due to outliers, provided that the outliers constitute less than 50% of the set, in other words, LTS is a robust estimator with a 50% breakdown point. The FAST-LTS algorithm is developed for the computation of the estimators. Furthermore, a robust method for the estimate of shrinkage parameters is suggested. Monté-Carlo simulation study demonstrates the merit of the new method in the aspect of solving the multicollinearity and sensitivity to outliers over the ordinary least squares estimation. Finally, an example of real data is given for illustration.

Suggested Citation

  • Hadi Emami & Korosh Arzideh, 2023. "Robust ridge estimator in censored semiparametric linear models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(17), pages 5989-6007, September.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:17:p:5989-6007
    DOI: 10.1080/03610926.2021.2023573
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