Conditional Joint Probability Distributions of First Exit Times to Overlapping Absorbing Sets of the Mixture of Markov Jump Processes

TL;DR

This paper derives new explicit formulas for the joint distribution of first exit times in a mixture of Markov jump processes with overlapping absorbing states, capturing heterogeneity and path dependence.

Contribution

It generalizes existing distributional properties and provides explicit identities for first exit times using intensity matrices and Bayesian updates.

Findings

Explicit distributional identities for first exit times are derived.

The formulas incorporate heterogeneity and path dependence.

Numerical examples illustrate the theoretical results.

Abstract

New results on conditional joint probability distributions of first exit times are presented for a continuous-time stochastic process defined as the mixture of Markov jump processes moving at different speeds on the same finite state space, while the mixture occurs at a random time. Such mixture was first proposed by Frydman \cite{Frydman2005} and Frydman and Schuermann \cite{Frydman2008} as a generalization of the mover-stayer model of Blumen et at. \cite{Blumen}, and was recently extended by Surya \cite{Surya2018}, in which explicit distributional identities of the process are given, in particular in the presence of an absorbing state. We revisit \cite{Surya2018} for a finite mixture with overlapping absorbing sets. The contribution of this paper is two fold. First, we generalize distributional properties of the mixture discussed in \cite{Frydman2008} and \cite{Surya2018}. Secondly, we give distributional identities of the first exit times explicitly in terms of intensity matrices of the underlying Markov processes and the Bayesian updates of switching probability and of the probability distribution of states, despite the fact that the mixture itself is non-Markov. They form non-stationary functions of time and have the ability to capture heterogeneity and path dependence when conditioning on the available information (either full or partial) of the process. In particular, initial profile of the distributions forms of a generalized mixture of multivariate phase-type distributions of Assaf et al. \cite{Assaf1984}. When the underlying processes move at the same speed, in which case the mixture becomes a simple Markov process, these features are removed, and the initial distributions reduce to \cite{Assaf1984}. Some explicit and numerical examples are discussed to illustrate the main results.