Space-fractional versions of the negative binomial and Polya-type processes

TL;DR

This paper introduces a novel space fractional negative binomial process, deriving its properties, extensions, multivariate form, and potential applications in population growth and epidemiology, along with a simulation algorithm.

Contribution

It presents the first comprehensive study of the space fractional negative binomial process, including its distribution, governing equations, extensions, multivariate version, and applications.

Findings

Derived one-dimensional distributions using generalized Wright functions

Established the process as a Lévy process with explicit Lévy measure

Proposed a simulation algorithm for the SFNB process

Abstract

In this paper, we introduce a space fractional negative binomial (SFNB) process by subordinating the space fractional Poisson process to a gamma subordinator. Its one-dimensional distributions are derived in terms of generalized Wright functions and their governing equations are obtained. It is a L\'evy process and the corresponding L\'evy measure is given. Extensions to the case of distributed order SFNB process, where the fractional index follows a two-point distribution, is analyzed in detail. The connections of the SFNB process to a space fractional Polya-type process is also pointed out. Moreover, we define and study a multivariate version of the SFNB obtained by subordinating a $d$-dimensional space-fractional Poisson process by a common independent gamma subordinator. Some applications of the SFNB process to the studies of population's growth and epidemiology are pointed out. Finally, we discuss an algorithm for the simulation of the SFNB process.