title: Multivariate Box-Cox transformations with applications to neurometric data.
abstract: The Box-Cox power transform methodology for achieving Gaussianity is developed for a variety of models useful in neurometric statistical analysis. Algorithms are proposed for estimating the optimal transformations in the univariate and multivariate cases. Their use is briefly illustrated with neurometric data.
authors.affiliation: Department of Neurocybernetics, National Center for Scientific Research, Havana, Cuba.
authors.forename: R
authors.initials: R
authors.lastname: Biscay Lirio
authors.forename: P A
authors.initials: PA
authors.lastname: Valdés Sosa
authors.forename: R D
authors.initials: RD
authors.lastname: Pascual Marqui
authors.forename: J C
authors.initials: JC
authors.lastname: Jiménez-Sobrino
authors.forename: A
authors.initials: A
authors.lastname: Alvarez Amador
authors.forename: L
authors.initials: L
authors.lastname: Galán Garcia
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001921:Brain / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological