论文检索

椭圆分布误差下因子分析的峰度调整AI 解读

SUMMARY Simple corrections to normal theory tests of fit of the factor analysis model are provided for the situation where common factors have arbitrary distributions and errors have a distribution from the elliptical class. It is shown that normal theory estimators are asymptotically efficient in this situation and appropriate modifications for the asymptotic covariance matrix of estimators are provided. Factor analysis models with structured error covariance matrices are…

因子分析及相关模型中统计推断的稳健性AI 解读

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…

因子分析及相关模型中统计推断的稳健性AI 解读

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…

摘要暂缺