Some Modifications for Choosing Ridge Parameters

Standard least square regression can produce estimates having a large mean squares error (MSE) when predictor variables are highly correlated or multicollinear. In this article, we propose four modifications to choose the ridge parameter (K) when multicollinearity exists among the columns of the design matrix. The proposed new estimators are extended versions of that suggested by Khalaf and Shukur (2005 Khalaf , G. , Shukur , G. ( 2005 ). Choosing ridge parameter for regression problems . Commun. Statist. A 34 : 1177 – 1182 . [CSA] [Taylor & Francis Online] , [Google Scholar]). The properties of these estimators are compared with those of Hoerl and Kennard (1970a Hoerl , A. E. , Kennard , R. W. ( 1970a ). Ridge regression: biased estimation for non-orthogonal problems . Tech. . 12 : 55 – 67 . [CSA] [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and the OLS using the MSE criterion. All estimators under consideration are evaluated using simulation techniques under certain conditions where a number of factors that may affect their properties have been varied. In addition, it is shown that at least one of the proposed estimators either has a smaller MSE than the others or is the next best otherwise.