การเปรียบเทียบวิธีการแก้ไขพหุสัมพันธ์ในการถดถอยเชิงเส้นพหุคูณ - เปรมวดี ชูไสว (จุฬาลงกรณ์มหาวิทยาลัย, 2005)
Compares the parameter estimations of multiple regression coefficient when multicollinearity is occurred. The method under consideration are : ordinary least square method (OLS), ridge regression method (RID), PRINCIPAL COMPONENT REGRESSION METHOD (PCR) and ordinary least square method of transforming X by Gram-Schmidt orthogonalization (OLS ORT). The criterion of comparison is a ratio of average mean square error of coefficient. In this study, there are four factors that should affect AMSE in coefficient estimation which are the number of independent variable, sample size, level of multicollinearity and standard error. Numbers of independent variables are 3, 6 and 9. Sample sizes of each independent variable have 5p, 10p, 15p, 20p, 25p, and 30p when p is number of independent variables. Error in the model are random variables sampling from normal distribution with zero mean and various standard deviation, ie. 1,5,10. Level of multicollinearity split into 3 levels (low, medium and high) The results of thisstudy is that OLS is the method, having the best efficiency for prediction in regression model if it has low level of multicollinearity and standard error equal to 1. Generally, the efficiency of PCR and OLS ORT are not different in all cases except case that have high level of multicollinearity. OLS ORT are the efficient methods if it has standard error equal to 5 or 10 and has medium multicollinearity. PCR is the best method if it has high multicollinearity in all level of standard error. The factors that affected AMSE in coefficient estimation which are ranked from the most are level of multicollinearity, standard error, the number of independent variables and sample size. The value of AMSE of 4 methods is increased when standard error increased or level of multicollinearity increased. Contradictory, the value of AMSE of all methods is decreased when sample size increased.