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
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.
2020
9788891910776
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/84241
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