Robust ridge estimator in censored semiparametric linear models

Hadi Emami and Korosh Arzideh

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 17, 5989-6007

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.

Date: 2023
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DOI: 10.1080/03610926.2021.2023573

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