Iterated Generalized Counting Process and its Extensions

TL;DR

This paper introduces the iterated generalized counting process (IGCP), explores its properties, extensions, and dependence structures, and discusses potential applications in modeling complex stochastic phenomena.

Contribution

The paper develops the distributional properties of IGCP, introduces several extensions, and analyzes their dependence structures and potential applications.

Findings

IGCP's distributional properties are derived.

Extensions like compound and multivariate IGCP are introduced.

Time-changed IGCP shows long-range dependence and non-infinite divisibility.

Abstract

In this paper, we study the composition of two independent GCPs which we call the iterated generalized counting process (IGCP). Its distributional properties such as the transition probabilities, probability generating function, state probabilities and its corresponding L\'evy measure are obtained. We study some integrals of the IGCP. Also, we study some of its extensions, for example, the compound IGCP, the multivariate IGCP and the $q$-iterated GCP. It is shown that the IGCP and the compound IGCP are identically distributed to a compound GCP which leads to their martingale characterizations. Later, a time-changed version of the IGCP is considered where the time is changed by an inverse stable subordinator. Using its covariance structure, we establish that the time-changed IGCP exhibits long-range dependence property. Moreover, we show that its increment process exhibits short-range dependence property. Also, it is shown that its one-dimensional distributions are not infinitely divisible. Initially, some of its potential real life applications are discussed.