การกำจัดตัวแปรอิสระแบบถอยหลังในตัวแบบโพรบิท - พรทิพย์ คำหล้า (จุฬาลงกรณ์มหาวิทยาลัย, 2010)
To investigate the efficiency of backward elimination pricedure and hocking s procedure for remedy of multicollinearity in the probit model. The data are transformed to data in multiple regression using Nordberg s transformation. The interested factors are number of independent varibles (p), sample size (n), degree of multicollinearity among independent variables (m), and proportion between amount of independent variables which are associated with dependent variables and non-associated with dependent varibles (a).The data are generated using Monte Carlo Techique through R-program. The independent variables are simulated having normal distribution, and the number of independent variables equal to 3, 4, 5, 6 (p=3, 4, 5, 6). The independent variables are designated to be 2 groups; a group associated with the dependent variables and a group non-associated with the dependent varibles. The samples size are set to be 20, 60, 100, 200 (n= 20, 60, 100, 200). The degree of multicollinearity among independent variables is designated to be 0.1, 0.5, 0.9 (M=0.1, 0.5, 0.9). The simulation is repeated 500 times in each situation. The percentage of mean error is used as the performance measure. The results show that as the number of independent variables equal to 3, 4, 5, the percentage of mean error of Hocking s Procedure is lower than the one from Backward Elimination. When the number of independent varibles is 6, the percentage of mean error increase for both methods in almost all of situations and the percentage of mean error of Backward Elimination is lower than the one from Hocking s procedure. In both methods, when sample size increases, percentage of mean error decreases