The concept of second- (and higher-) order interaction is widely used in categorical data analysis, where it proves useful for explaining the interdependence among three (or more) variables. Its use seems to be less common for continuous multivariate distributions, most likely owing to the predominant role of the Multivariate Normal distribution, for which any interaction involving more than two variables is necessarily zero. In this paper we explore the usefulness of a second-order interaction measure for studying the interdependence among three continuous random variables, by applying it to a trivariate Generalized Gamma distribution proposed by Bologna(2000)
BOLOGNA S, LOVISON G (2004). Second-order interaction in a Trivariate Generalized Gamma Distribution. In BOCK H.-H.CHIODI M. MINEO A. EDITORS (a cura di), Advances in Multivariate Data Analysis (pp. 219-231). HEIDELBERG : Springer.
Second-order interaction in a Trivariate Generalized Gamma Distribution
BOLOGNA, Salvatore;LOVISON, Gianfranco
2004-01-01
Abstract
The concept of second- (and higher-) order interaction is widely used in categorical data analysis, where it proves useful for explaining the interdependence among three (or more) variables. Its use seems to be less common for continuous multivariate distributions, most likely owing to the predominant role of the Multivariate Normal distribution, for which any interaction involving more than two variables is necessarily zero. In this paper we explore the usefulness of a second-order interaction measure for studying the interdependence among three continuous random variables, by applying it to a trivariate Generalized Gamma distribution proposed by Bologna(2000)
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