A Comparative Study On Some Methods For HandlingudMulticollinearity Problems

摘要

In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. A frequent obstacle is that several of the explanatory variables will vary in rather similar ways. As a result, their collective power of explanationudis considerably less than the sum of their individual powers. This phenomenon called multicollinearity, is a common problem in regression analysis. Handling multicollinearity problem in regression analysis is important because least squares estimations assumeudthat predictor variables are not correlated with each other. The performances of ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLSR) in handling multicollinearity problem in simulated data sets are compared to help and give future researchers a comprehensive view about the bestudprocedure to handle multicollinearity problems. PCR is a combination of principal component analysis (PCA) and ordinary least squares regression (OLS) while PLSRudis an approach similar to PCR because a component that can be used to reduce the number of variables need to be constructed. RR on the other hand is the modifiedudleast square method that allows a biased but more precise estimator. The algorithm is described and for the purpose of comparing the three methods, simulated data setsudwhere the number of cases were less than the number of observations used. The goal was to develop a linear equation that relates all the predictor variables to a response variable. For comparison purposes, mean square errors (MSE) were calculated. A Monte Carlo simulation study was used to evaluate the effectiveness of these threeudprocedures. The analysis including all simulations and calculations were done using statistical package S-Plus 2000 software.ud