One of the main goals of the multiple linear regression model, Y = Xβ + u, is to assess the importance of independent variables in determining their predictive ability. However, in practical applications, inference about the coefficients of regression can be difficult because the independent variables are correlated and multicollinearity causes instability in the coefficients. A new estimator of ridge regression parameter is proposed and evaluated by simulation techniques in terms of mean squares error (MSE). Results of the simulation study indicate that the suggested estimator dominates ordinary least squares (OLS) estimator and other ridge estimators with respect to MSE.
Khalaf, Ghadban and Iguernane, Mohamed
"Multicollinearity and A Ridge Parameter Estimation Approach,"
Journal of Modern Applied Statistical Methods: Vol. 15
, Article 25.
Available at: http://digitalcommons.wayne.edu/jmasm/vol15/iss2/25