Estimation of generalized structured component analysis models with alternating least squares
Rainer Schlittgen ()
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Rainer Schlittgen: University of Hamburg
Computational Statistics, 2018, vol. 33, issue 1, 527-548
Abstract This paper presents a new algorithm estimating structural equation models by using the generalized structured component analysis (GSCA) formulation. In GSCA models, the latent variables are composites. The weights are estimated in conjunction with the other model parameters, optimizing a genuine least squares criterion. The new algorithm is designed for three subclasses often used in applications. Linear regressions are alternated for different sets of parameters according to the nonlinear manner in which the parameters are incorporated in the model. The new algorithm is compared with two existing ones by using different examples from the literature, as well as simulations. On the average the proposed algorithm produces better criterion values compared to the other two ones.
Keywords: Algorithm; Fit criterion; GSCA; PLS (search for similar items in EconPapers)
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