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均值与协方差结构分析的理论和实践改进AI 解读

The most widely used multivariate statistical models in the social and behavioral sciences involve linear structural relations among observed and latent variables. In practice, these variables are generally nonnormally distributed; hence classical multivariate analysis, based on multinomial error-free variables having no simultaneous interrelations, is not adequate to deal with such data. A promising alternative, based on asymptotically distribution-free (ADF) covariance…

椭圆总体中基于伪最大似然估计的统计推断AI 解读

In this article we develop statistical inference based on the maximum likelihood method in elliptical populations with an unknown density function. The method assuming the multivariate normal distribution, using the sample mean and the sample covariance matrix, is basically correct even for elliptical populations under a certain kurtosis adjustment, but is not statistically efficient, especially when the kurtosis of the population distribution has higher than moderate…

具有异质峰度参数的协方差结构分析AI 解读

This paper discusses the analysis of covariance structures in a wide class of multivariate distributions whose marginal distributions may have heterogeneous kurtosis parameters. Elliptical distributions often used as a generalization of the normal theory are members of this class. It is shown that a simple adjustment of the weight matrix of normal theory, using kurtosis estimates, results in an asymptotically efficient estimator of structural parameters within the class of…

具有异质峰度参数的协方差结构分析AI 解读

This paper discusses the analysis of covariance structures in a wide class of multivariate distributions whose marginal distributions may have heterogeneous kurtosis parameters. Elliptical distributions often used as a generalization of the normal theory are members of this class. It is shown that a simple adjustment of the weight matrix of normal theory, using kurtosis estimates, results in an asymptotically efficient estimator of structural parameters within the class of…