A common multi-group Redundancy Analysis is introduced, when the reduced space is given by a singular value decomposition of a multivariate best linear predictor. The algorithm finds a nearby OLS fixed-effects estimates by a least squares closed-form solution, provided by the standardized predictor. The empirical predictor is given by an extension of the distribution-free variance least squares method to an iterative multivariate response algorithm.
Joint Redundancy Analysis by a Multivariate Linear Predictor
Renato Salvatore
;MARCIS, Laura
2020-01-01
Abstract
A common multi-group Redundancy Analysis is introduced, when the reduced space is given by a singular value decomposition of a multivariate best linear predictor. The algorithm finds a nearby OLS fixed-effects estimates by a least squares closed-form solution, provided by the standardized predictor. The empirical predictor is given by an extension of the distribution-free variance least squares method to an iterative multivariate response algorithm.File in questo prodotto:
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Pearson-SIS-2020-atti-convegno.pdf
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Joint_Redundancy_Analysis_by_a_multivariate_linear_predictor_revised_final.pdf
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Descrizione: Contributo in Atti di Convegno
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