Advances and Applications in Statistics

The Advances and Applications in Statistics is an internationally recognized journal indexed in the Emerging Sources Citation Index (ESCI). It provides a platform for original research papers and survey articles in all areas of statistics, both computational and experimental in nature.

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A HIERARCHICAL MODELLING FOR MARKED POINT PROCESS GENERATED BY A MARKOV CHAIN

Authors

  • Shaochuan Lu

Keywords:

marked point process, Markov modulated Poisson process, Neyman-Scott cluster process, EM Algorithm

DOI:

https://doi.org/10.17654/0972361725073

Abstract

We introduce a stochastic model for the marked point process generated from a Markov chain, with the ground process formed by an MMPP and the associated marks serving as a subsidiary process. The model formulation is motivated for two applications: modelling insurance claims and the occurrence of earthquakes.

Received: July 3, 2025
Accepted: August 6, 2025

References

[1] D. J. Daley and D. Vere-Jones, An Introduction to the Theory of Point Processes, Vol. I, Springer, 2003.

[2] G. Dionne and C. Vanasse, A generalization of actuarial automobile insurance rating models: the negative Binomial distribution with a regression component, ASTIN Bulletin 19 (1989), 199-212.

[3] W. Fischer and K. Meier-Hellstern, The Markov-modulated Poisson process (MMPP) cookbook, Performance Evaluation 18 (1992), 149-171.

[4] E. W. Frees and E. A. Valdez, Hierarchical insurance claims modeling, Journal of the American Statistical Association 103(484) (2008), 1457-1469.

[5] Y. Ogata, Statistical models for earthquake occurrences and residual analysis for point processes, J. Amer. Statist. Soc. 83(401) (1988), 9-27.

[6] Y. Ogata, Space-time point-process models for earthquake occurrences, Annals of the Institute of Statistical Mathematics 24 (1998), 379-402.

[7] S. Lu, Markov modulated Poisson process associated with state-dependent marks and its applications to the deep earthquakes, Annals of the Institute of Statistical Mathematics 64 (2012), 87-106.

[8] X. L. Meng and D. B. Rubin, Maximum likelihood estimation via the ECM algorithm: a general framework, Biometrika 80 (1993), 267-278.

[9] J. Neyman and E. L. Scott, A statistical approach to problems of cosmology, J. Royal. Stat. Soc. Ser. B 20 (1958), l-43.

[10] T. Rydén, An EM algorithm for estimation in Markov-modulated Poisson processes, Computational Statistics and Data Analysis 21 (1996), 431-447.

Published

17-11-2025

Issue

Section

Articles

How to Cite

A HIERARCHICAL MODELLING FOR MARKED POINT PROCESS GENERATED BY A MARKOV CHAIN. (2025). Advances and Applications in Statistics , 92(12), 1753-1766. https://doi.org/10.17654/0972361725073

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