期刊名称：International Journal of Statistics and Applications
印刷版ISSN：2168-5193
电子版ISSN：2168-5215
出版年度：2018
卷号：8
期号：4
页码：167-172
DOI：10.5923/j.statistics.20180804.02
语种：English
出版社：Scientific & Academic Publishing Co.
摘要：This study aims to compare the performance of Ordinary Least Square (OLS), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression (RR) and Principal Component Regression (PCR) methods in handling severe multicollinearity among explanatory variables in multiple regression analysis using data simulation. In order to select the best method, a Monte Carlo experiment was carried out, it was set that the simulated data contain severe multicollinearity among all explanatory variables (ρ = 0.99) with different sample sizes (n = 25, 50, 75, 100, 200) and different levels of explanatory variables (p = 4, 6, 8, 10, 20). The performances of the four methods are compared using Average Mean Square Errors (AMSE) and Akaike Information Criterion (AIC). The result shows that PCR has the lowest AMSE among other methods. It indicates that PCR is the most accurate regression coefficients estimator in each sample size and various levels of explanatory variables studied. PCR also performs as the best estimation model since it gives the lowest AIC values compare to OLS, RR, and LASSO.
关键词：Multicollinearity; LASSO; Ridge Regression; Principal Component Regression
