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In this article, two-parameter estimators in linear model with multicollinearity are considered. An alternative efficient two-parameter estimator is proposed and its properties are examined. Furthermore, this was compared with the ordinary least squares (OLS) estimator and ordinary ridge regression (ORR) estimators. Also, using the mean squares error criterion the proposed estimator performs more efficiently than OLS estimator, ORR estimator and other reviewed two-parameter estimators. A numerical example and simulation study are finally conducted to illustrate the superiority of the proposed estimator.

This paper proposes an adjusted ridge regression estimator for b for the linear regression model. The merit of the proposed estimator is that it does not require estimating the ridge parameter k unlike other existing estimators. We compared our estimator with an ordinary least squares (LS) estimator and with some well known estimators proposed by Hoerl and Kennard (1970), ordinary ridge regression (RR) estimator and generalized ridge regression (GR) and some estimators proposed by Kibria (2003) among others. A simulation study has been conducted and compared for the performance of the estimators in the sense of smaller mean square error (MSE). It appears that the proposed estimator is promising and can be recommended to the practitioners. ª 2015 The Author. Production and hosting by Elsevier B.V. on behalf of University of Bahrain. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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