ASYMPTOTIC ROBUSTNESS IN MULTIPLE GROUP LINEAR-LATENT VARIABLE MODELS
ASYMPTOTIC ROBUSTNESS IN MULTIPLE GROUP LINEAR-LATENT VARIABLE MODELS
复制标题
多群线性潜变量模型的渐近鲁棒性
作者：
A. Satorra
分析线性潜变量模型的标准方法依赖于观测变量呈正态分布的假设。正态性允许仅基于一阶矩和二阶矩进行统计推断。一般来说，非正态分布数据的推论需要三阶矩和四阶矩矩阵的估计。在本文中，我们表明，基于正态理论的推论在允许相当大地偏离正态性的一般假设下保留了有效性和渐近效率。特别是，当用仅依赖于数据叉积矩的矩阵替换高阶矩矩阵时，我们获得了获得正确渐近推论的条件。
Standard methods for analyzing linear-latent variable models rely on the assumption that the observed variables are normally distributed. Normality allows statistical inferences to be carried out based solely on the first-and second-order moments. In general, inferences for nonnormally distributed data require the estimates of matrices of third-and fourth-order moments. In the present paper, we show that inferences based on normal theory retain validity and asymptotic efficiency under general assumptions that allow for considerable departure from normality. In particular, we obtain conditions under which correct asymptotic inferences are attained when replacing a matrix of higher order moments by a matrix that depends only on cross-product moments of the data.
DOI：
10.1111/j.2044-8317.1984.tb00789.x
发表时间：
1984-01-01
影响因子：
2.6
作者：
BROWNE, MW
通讯作者：
BROWNE, MW