Multivariate and Matrix-Variate Logistic Models in the Real and Complex Domains

A. M. Mathai ()
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A. M. Mathai: Department of Mathematics and Statistics, McGill University, Montreal, QC H3A 0G4, Canada

Stats, 2024, vol. 7, issue 2, 1-17

Abstract: Several extensions of the basic scalar variable logistic density to the multivariate and matrix-variate cases, in the real and complex domains, are given where the extended forms end up in extended zeta functions. Several cases of multivariate and matrix-variate Bayesian procedures, in the real and complex domains, are also given. It is pointed out that there are a range of applications of Gaussian and Wishart-based matrix-variate distributions in the complex domain in multi-look data from radar and sonar. It is hoped that the distributions derived in this paper will be highly useful in such applications in physics, engineering, statistics and communication problems, because, in the real scalar case, a logistic model is seen to be more appropriate compared to a Gaussian model in many industrial applications. Hence, logistic-based multivariate and matrix-variate distributions, especially in the complex domain, are expected to perform better where Gaussian and Wishart-based distributions are currently used.

Keywords: multivariate distributions; matrix-variate case; real and complex domains; model buildings; Bayesian analysis; extended zeta functions (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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