Lee S Y, Poon W Y, Bentler P M
Department of Statistics, Chinese University of Hong Kong, Shatin, NT, Hong Kong.
Br J Math Stat Psychol. 1995 Nov;48 ( Pt 2):339-58. doi: 10.1111/j.2044-8317.1995.tb01067.x.
This paper develops a computationally efficient procedure for analysis of structural equation models with continuous and polytomous variables. A partition maximum likelihood approach is used to obtain the first stage estimates of the thresholds and the polyserial and polychoric correlations in the underlying correlation matrix. Then, based on the joint asymptotic distribution of the first stage estimator and an appropriate weight matrix, a generalized least squares approach is employed to estimate the structural parameters in the correlation structure. Asymptotic properties of the estimators are derived. Some simulation studies are conducted to study the empirical behaviours and robustness of the procedure, and compare it with some existing methods.
本文开发了一种计算效率高的程序，用于分析具有连续变量和多分类变量的结构方程模型。采用一种分割最大似然方法来获得潜在相关矩阵中阈值、多系列相关和多向相关的第一阶段估计值。然后，基于第一阶段估计量的联合渐近分布和一个适当的权重矩阵，采用广义最小二乘法来估计相关结构中的结构参数。推导了估计量的渐近性质。进行了一些模拟研究，以考察该程序的实证行为和稳健性，并将其与一些现有方法进行比较。
