Addressing the problem of Multicollinearity in parametric model using some shrinkage methods


Abstract Multicollinearity has been a serious problem in analysis of Regression , The ordinary least squares method (OLS) may result in high variability in the estimates of the regression coefficients in the presence of multicollinearity .To address this problem using some shrinkage methods for estimation general linear model (GLM) it’s ( Lasso and Elastic- Net methods) and this methods reduces the variability of the estimation by shrinkage the coefficients and at the same time produces interpretable models by shrinkage some coefficients to exactly zero.In this research show performance these methods in serious multicollinearity by Application on real data and reach to beast method based on mean squares error (MSE) and it’s ( Elastic- Net ) method . All results were obtained depend on (SPSS) program .