PLS路径模型的新估计方法
New Estimation Procedures for PLS Path Modelling
摘要：
给定R组的数值变量X1，. XR，我们假设每个组是一个潜在潜在变量的结果，并且所有潜在变量通过线性方程组绑定在一起。此外，我们假设一些解释性潜变量可能在一个或多个方程中成对相互作用。我们基本上考虑PLS路径建模的算法来估计潜在变量和模型的系数。新的“外部”估计方案，提出了一个更灵活的方式向强组结构的潜在变量。提出了新的“内部”估计方案，使PLSPM能够充分利用变量组互补性并处理相互作用。给出了应用实例。
英文摘要：
Given R groups of numerical variables X1, ... XR, we assume that each group is the result of one underlying latent variable, and that all latent variables are bound together through a linear equation system. Moreover, we assume that some explanatory latent variables may interact pairwise in one or more equations. We basically consider PLS Path Modelling's algorithm to estimate both latent variables and the model's coefficients. New "external" estimation schemes are proposed that draw latent variables towards strong group structures in a more flexible way. New "internal" estimation schemes are proposed to enable PLSPM to make good use of variable group complementarity and to deal with interactions. Application examples are given.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
