A new robust ridge parameter estimator based on search method for linear regression model

Atila Göktaş, Özge Akkuş and Aykut Kuvat

Journal of Applied Statistics, 2021, vol. 48, issue 13-15, 2457-2472

Abstract: A large and wide variety of ridge parameter estimators proposed for linear regression models exist in the literature. Actually proposing new ridge parameter estimator lately proving its efficiency on few cases seems endless. However, so far there is no ridge parameter estimator that can serve best for any sample size or any degree of collinearity among regressors. In this study we propose a new robust ridge parameter estimator that serves best for any case assuring that is free of sample size, number of regressors and degree of collinearity. This is in fact realized by choosing three best from enormous number of ridge parameter estimators performing well in different cases in developing the new ridge parameter estimator in a way of search method providing the smallest mean square error values of regression parameters. After that a simulation study is conducted to show that the proposed parameter is robust. In conclusion, it is found that this ridge parameter estimator is promising in any case. Moreover, a recent data set is used as an example for illustration to show that the proposed ridge parameter estimator is performing better.

Date: 2021
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DOI: 10.1080/02664763.2020.1803814

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