ตัวแบบโพรบิตแบบ 2 ประเภท สำหรับการพยากรณ์การจำแนกข้อมูลไม่จำกัดกลุ่ม - ชลลดา กล้วยไม้ (จุฬาลงกรณ์มหาวิทยาลัย, 2010)
The objective of this study is to find out the optimal cut-off point for predictive classification of ungrouped data using binary probit model. The interesting factors are the number of independent variables (p) with 3 levels; low level (p=1, 2), medium level (p= 3, 4) and high level (p=5,6), the sample size (n) with 3 levels ; low level (n=20,40), medium level (n=60,80) and high level (n=100,120), the failure rate (a) of the values 0.1,0.5 and 0.9 and the degree of multicollinearity among independent variables (M) of the values 0, 0.33, 0.67 and 0.99. The data in all situations are generated using Monte Carlo technique through R-program. The cut-off point is captured using Hadjicostas P. (2006) theory. The results can be summarized as follow: As the number of independent variables changed but the other factors are kept constant, with the failure rate equal to 0.1 and 0.9, the optimal cut-off point converges approximately to 0.5 as the failure rate equals to 0.5. As sample size changed but the other factors are kept constant, with the failure rate equals to 0.5, the optimal cut-off point converges approximately to 0.5. For the other situations, the optimal cut-off point are lower than 0.5. As the failure rate in data set changed but the other factors are kept constant, with the failure rate equals to 0.5, the optimal cut-off point is the highest and converges approximately to 0.5. As the degree of multicollinearity among independent variables changed but the other factors are kept constant, with the failure rate equals to 0.5, the optimal cut-off point decreases from 0.5. For the other situations, the optimal cut-off point decreases to M =0.67 then slightly increases. Finally the estimated binary probit model with all interaction terms is needed to find the estimated cut-off point for all situations. The R² is needed to measure the goodness-of-fit of the estimated model. From the estimated regression equation, the optimal cut-off point for any situation can be predicted