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Given R groups of numerical variables X1, ... XR, we assume that each groupis the result of one underlying latent variable, and that all latent variablesare bound together through a linear equation system. Moreover, we assume thatsome explanatory latent variables may interact pairwise in one or moreequations. We basically consider PLS Path Modelling's algorithm to estimateboth latent variables and the model's coefficients. New "external" estimationschemes are proposed that draw latent variables towards strong group structuresin a more flexible way. New "internal" estimation schemes are proposed toenable PLSPM to make good use of variable group complementarity and to dealwith interactions. Application examples are given.

New Estimation Procedures for PLS Path Modelling