Multicollinearity often occurs in regression analysis. Multicollinearity is a condition of correlation between independent variables which is a problem. One method that can overcome multicollinearity is the LASSO (Least Absolute Shrinkage and Selection Operator) method. LASSO is able to help to shrink multicollinearity and improve the accuracy of linear regression models. Estimators of LASSO parameters can be solved by the LARS (Least Angle Regression and Shrinkage) algorithm by algorithm which calculates the correlation vector, the largest absolute correlation value, equiangular vector, inner product vector, and determines the LARS algorithm limiter for LASSO. Selecting the best model using the Mallow’s C_p statistics. The smallest Mallow’s C_p value will be selected as the best model. LASSO method with a more detailed procedure with LARS algorithm and selecting the best model using the Mallow’s C_p statistics is discussed in this paper.
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