On Performance of Shrinkage Methods - A Monte Carlo Study

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Faculty of Sciences, Federal University of Technology Minna, Nigeria.


Multicollinearity has been a serious problem in regression analysis, Ordinary Least Squares (OLS) regression may result in high variability in the estimates of the regression coefficients in the presence of multicollinearity. Least Absolute Shrinkage and Selection Operator (LASSO) methods is a well established method that reduces the variability of the estimates by shrinking the coefficients to exactly zero. We present the performance of LASSO-type estimators in the presence of multicollinearity using Monte Carlo approach. The performance of LASSO, Adaptive LASSO, Elastic Net, Fused LASSO and Ridge Regression (RR) in the presence of multicollinearity in simulated data sets are compared using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) criteria. A Monte Carlo experiment of 1000 trials was carried out at different sample sizes n (50, 100 and 150) with different levels of multicollinearity among the exogenous variables (p = 0.3, 0.6, and 0.9). The overall performance of LASSO appears to be the best but Elastic net tends to be more accurate when the sample size is large.



Multicollinearity, Elastic net, Ridge, Adaptive Lasso, Fused Lasso


Journal of Science, Technology and Education (JOSTMED)