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Abstract: 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.



Author: Xavier Bry I3M

Source: https://arxiv.org/







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