Consistent Partial Least Squares for Nonlinear Structural Equation Models
Consistent Partial Least Squares for Nonlinear Structural Equation Models
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DOI：
10.1007/s11336-013-9370-0
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发表时间：
2013-12
期刊：
影响因子：
3
通讯作者：
T. Dijkstra;K. Schermelleh-engel
中科院分区：
文献类型：
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作者：
T. Dijkstra;K. Schermelleh-engel
众所周知，偏最小二乘法应用于具有潜在变量的模型（通过指标间接测量）是不一致的。 PLS 替代潜变量的指标线性复合不服从后者所满足的方程。我们提出了简单的非迭代修正，从而为载荷和潜在变量之间的相关性提供一致且渐近正态（CAN）估计器。此外，我们还展示了如何获得包含线性和交互项的结构递归方程组参数的 CAN 估计器，而无需指定特定的联合分布。如果包含二次项和高阶项，当预测变量和误差项共同为正态时，该方法也将生成 CAN 估计器。我们使用蒙特卡洛研究和实证应用，将调整后的 PLS（用 PLSc 表示）与潜在调节结构方程 (LMS) 进行比较。
Partial Least Squares as applied to models with latent variables, measured indirectly by indicators, is well-known to be inconsistent. The linear compounds of indicators that PLS substitutes for the latent variables do not obey the equations that the latter satisfy. We propose simple, non-iterative corrections leading to consistent and asymptotically normal (CAN)-estimators for the loadings and for the correlations between the latent variables. Moreover, we show how to obtain CAN-estimators for the parameters of structural recursive systems of equations, containing linear and interaction terms, without the need to specify a particular joint distribution. If quadratic and higher order terms are included, the approach will produce CAN-estimators as well when predictor variables and error terms are jointly normal. We compare the adjusted PLS, denoted by PLSc, with Latent Moderated Structural Equations (LMS), using Monte Carlo studies and an empirical application.