An Overview of the Pathway Idea in Statistical and Physical Sciences

TL;DR

The paper introduces the pathway model, a flexible switching mechanism connecting various statistical distributions and physical models, capable of capturing stable, unstable, and transitional phenomena across disciplines.

Contribution

It presents the pathway model as a unifying framework linking diverse distributions, statistical measures, and physical theories, extending existing results to broader classes of populations.

Findings

Connects beta and gamma families via the pathway parameter

Encompasses Tsallis statistics and superstatistics as special cases

Extends quadratic form results to wider population classes

Abstract

Pathway idea is a switching mechanism by which one can go from one functional form to another, and to yet another. It is shown that through a parameter $\alpha$, called the pathway parameter, one can connect generalized type-1 beta family of densities, generalized type-2 beta family of densities, and generalized gamma family of densities, in the scalar as well as the matrix cases, also in the real and complex domains. It is shown that when the model is applied to physical situations then the current hot topics of Tsallis statistics and superstatistics in statistical mechanics become special cases of the pathway model, and the model is capable of capturing many stable situations as well as the unstable or chaotic neighborhoods of the stable situations and transitional stages. The pathway model is shown to be connected to generalized information measures or entropies, power law, likelihood ratio criterion or $\lambda-$criterion in multivariate statistical analysis, generalized Dirichlet densities, fractional calculus, Mittag-Leffler stochastic process, Kr\"{a}tzel integral in applied analysis, and many other topics in different disciplines. The pathway model enables one to extend the current results on quadratic and bilinear forms, when the samples come from Gaussian populations, to wider classes of populations.