การเปรียบเทียบเกณฑ์การคัดเลือกจำนวนปัจจัยในการวิเคราะห์ปัจจัยเชิงสถิติ - พรรณนิภา รินทระ (จุฬาลงกรณ์มหาวิทยาลัย, 2009)
To compare the performance among different criteria for determining the number of factors in the statistical factor analysis. The criteria under this study includes 10-fold Likelihood Cross-Validation (LCV), Akaike’s Information Criteria (AIC), Schwarz’s Information Criteria (SIC) and Hannan and Quinn’s Information Criteria (HQ). We adopt the percentage of the accuracy as the performance measure. The data are generated from the multivariate normal distribution with mean vector o and variance 1. The correlation matrix is sampled from the uniform distribution on all possible correlation matrices. The number of variables (p) are 10, 20, 30 and 40. The number of factors are 1, 2, .., (p/2). The sample size are 200, 300, 500 and 1,000. The conclusions are as follows: 1.Case of 10 variables: If the number of factors is less than or equal to 20 percent of the number of variables, the performances of all criteria are the same. If the number of factors is greater than 20 percent of the number of variables, SIC is the best criteria followed by LCV, which are not different from HQ, and AIC is the worst criteria. 2.Cases of 20, 30 and 40 variables: If the number of factors is less than or equal to 22.5 percent of the number of variables, the performances of all criteria are the same. If the number of factors is greater than 22.5 percent of the number of variables but less than or equal to 35.83 percent of the number of variables, SIC is the best criteria followed by HQ, LCV and AIC, respectively. If the number of factors is greater than 35.85 percent of the number of variables, SIC is the best criteria followed by the LCV, the HQ and the AIC, respectively.