Partial least squares path modeling has been widely used for component-based structural equation modeling, where constructs are represented by weighted composites or components of observed variables. This approach remains a limited-information method that carries out two separate stages sequentially to estimate parameters (component weights, loadings, and path coefficients), indicating that it has no single optimization criterion for estimating the parameters at once. In general, limited-information methods are known to provide less efficient parameter estimates than full-information ones. To address this enduring issue, we propose a full-information method for partial least squares path modeling, termed global least squares path modeling, where a single least squares criterion is consistently minimized via a simple iterative algorithm to estimate all the parameters simultaneously. We evaluate the relative performance of the proposed method through the analyses of simulated and real data. We also show that from algorithmic perspectives, the proposed method can be seen as a block-wise special case of another full-information method for component-based structural equation modeling-generalized structured component analysis.

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全局最小二乘路径建模：偏最小二乘路径建模的全信息替代方案

偏最小二乘路径建模已广泛用于基于分量的结构方程建模，其中构造由加权复合或观察变量的分量表示。这种方法仍然是一种信息有限的方法，它依次执行两个单独的阶段来估计参数（组件权重、载荷和路径系数），这表明它没有用于一次估计参数的单一优化标准。一般来说，已知有限信息方法提供的参数估计效率低于全信息方法。为了解决这个持久的问题，我们提出了一种用于偏最小二乘路径建模的全信息方法，称为全局最小二乘路径建模，其中单个最小二乘准则通过简单的迭代算法一致地最小化，以同时估计所有参数。我们通过对模拟数据和真实数据的分析来评估所提出方法的相对性能。我们还表明，从算法的角度来看，所提出的方法可以看作是另一种基于组件的结构方程建模的全信息方法——广义结构化组件分析的逐块特例。