Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework

Benjamin Avanzi, Greg Taylor, Bernard Wong and Alan Xian

European Journal of Operational Research, 2021, vol. 290, issue 1, 177-195

Abstract: The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we extend the Markov-modulated Poisson process framework through the introduction of a flexible frequency perturbation measure. This contribution enables known information of observed event arrivals to be naturally incorporated in a tractable manner, while the hidden Markov chain captures the effect of unobservable drivers of the data. In addition to increases in accuracy and interpretability, this method supplements analysis of the latent factors. Further, this procedure naturally incorporates data features such as over-dispersion and autocorrelation. Additional insights can be generated to assist analysis, including a procedure for iterative model improvement.

Keywords: Risk analysis; Markov processes; Count processes; Data analysis; EM algorithm (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:290:y:2021:i:1:p:177-195

DOI: 10.1016/j.ejor.2020.07.022

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