On the Asymptotic Optimality of Alternative Minimum-Distance Estimators in Linear Latent-Variable Models
On the Asymptotic Optimality of Alternative Minimum-Distance Estimators in Linear Latent-Variable Models
复制标题
线性潜变量模型中替代最小距离估计的渐近最优性
作者：
A. Satorra;H. Neudecker
在线性潜变量模型的背景下，和一般类型的分布的数据，最小距离估计的权重矩阵只使用二阶矩的子向量的渐近最优性进行了研究。渐近最优性扩展到整个向量的参数估计，如果施加额外的限制的三阶矩的变量。正常（伪）最大似然方法的最优性相关的结果也包括在内。导出的结果涉及广泛的潜变量模型和估计方法，经常使用的软件，如LISREL，EQS和CALIS的潜变量模型的分析。一般的结果是专门的背景下，多元回归和变量中的误差的联立方程。
In the context of linear latent-variable models, and a general type of distribution of the data, the asymptotic optimality of a subvector of minimum-distance estimators whose weight matrix uses only second-order moments is investigated. The asymptotic optimality extends to the whole vector of parameter estimators, if additional restrictions on the third-order moments of the variables are imposed. Results related to the optimality of normal (pseudo) maximum likelihood methods are also encompassed. The results derived concern a wide class of latent-variable models and estimation methods used routinely in software for the analysis of latent-variable models such as LISREL, EQS, and CALIS. The general results are specialized to the context of multivariate regression and simultaneous equations with errors in variables.
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