Data on Operations Science Described by Researchers at University of New South Wales Sydney (Modelling and Understanding Count Processes Through a Markov-modulated Non-homogeneous Poisson Process Framework)

2021 APR 07 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Science Daily -- Investigators publish new report on Science - Operations Science. According to news reporting out of Sydney, Australia, by NewsRx editors, research stated, “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.”

Funders for this research include Australian Research Council, Allianz Australia Insurance Ltd, Insurance Australia Group Ltd, Suncorp Metway Ltd, Australian Government, UNSW Business School.

Our news journalists obtained a quote from the research from the University of New South Wales Sydney, “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. Implementation difficulties are also addressed with a focus on dealing with large data sets, where latent models are especially advantageous due the large number of observations facilitating identification of hidden factors. Namely, computational issues such as numerical underflow and high processing cost arise in this context and in this paper, we produce procedures to overcome these problems.”

According to the news editors, the research concluded: “This modelling framework is demonstrated using a large insurance data set to illustrate theoretical, practical and computational contributions and an empirical comparison to other count models highlight the advantages of the proposed approach.”

This research has been peer-reviewed.

For more information on this research see: Modelling and Understanding Count Processes Through a Markov-modulated Non-homogeneous Poisson Process Framework. European Journal of Operational Research, 2021;290(1):177-195. European Journal of Operational Research can be contacted at: Elsevier, Radarweg 29, 1043 Nx Amsterdam, Netherlands. (Elsevier - www.elsevier.com; European Journal of Operational Research - http://www.journals.elsevier.com/european-journal-of-operational-research/)

Our news journalists report that additional information may be obtained by contacting Alan Xian, University of New South Wales Sydney, University of New South Wales, Sch Risk & Actuarial Studies, Sydney, Nsw 2052, Australia. Additional authors for this research include Benjamin Ayanzi, Greg Taylor and Bernard Wong.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.ejor.2020.07.022. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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