Robustness of Statistical Inference in Factor Analysis and Related Models
研究了一类潜变量模型（包括无限制因子分析模型），证明在共同因子非正态但唯一因子服从多元正态分布时，基于正态假设的最小差异检验统计量和估计量仍保持渐近性质。
A class of latent variable models which includes the unrestricted factor analysis model is considered. It is shown that minimum discrepancy test statistics and estimators derived under normality assumptions retain their asymptotic properties when the common factors are not normally distributed but the unique factors do have a multivariate normal distribution. The minimum discrepancy test statistics and estimators considered include the usual likelihood ratio test statistic and maximum likelihood estimators.