Combating Multicollinearity: A New Two-Parameter Approach

The ordinary least square (OLS) estimator is the Best Linear Unbiased Estimator (BLUE) when all linear regression model assumptions are valid. OLS estimator, however, becomes inefficient in presence of multicollinearity. Various one and two-parameter estimators have been proposed to circumvent problem This paper presents a new twoparameter called Liu-Kibria Lukman (LKL) estimator. compared theoretically through Monte Carlo simulation with existing such as square, ridge regression, Liu, Kibria-Lukman, Modified Ridge estimators. results show that performs better than considered this study under some conditions, using mean error criterion. A real-life application Portland cement Longley datasets supported theoretical by giving smallest