On the Asymptotic Optimality of Alternative Minimum-Distance Estimators in Linear Latent-Variable Models
研究了线性潜变量模型中，仅使用二阶矩的加权矩阵的最小距离估计量子向量的渐近最优性，并推广到全参数向量，结果适用于LISREL等常用软件中的估计方法。
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.