IDEAS home Printed from https://ideas.repec.org/a/spr/metcap/v25y2023i1d10.1007_s11009-023-10004-7.html
   My bibliography  Save this article

Normal Approximation for Fire Incident Simulation Using Permanental Cox Processes

Author

Listed:
  • Dawud Thongtha

    (King Mongkut’s University of Technology Thonburi)

  • Nathakhun Wiroonsri

    (King Mongkut’s University of Technology Thonburi)

Abstract

Estimating the number of natural disasters benefits the insurance industry in terms of risk management. However, the estimation process is complicated due to the fact that there are many factors affecting the number of such incidents. In this work, we propose a Normal approximation technique for associated point processes for estimating the number of natural disasters under the following two assumptions: 1) the incident counts in any two distinct areas are positively associated and 2) the association between these counts in two distinct areas decays exponentially with respect to distance outside some small local neighborhood. Under the stated assumptions, we extend previous results for the Normal approximation technique for associated point processes, i.e., the establishment of non-asymptotic $$L^1$$ L 1 bounds for the functionals of these processes. Then we apply this new result to permanental Cox processes that are known to be positively associated. Finally, we apply our Normal approximation results for permanental Cox processes to Thailand’s fire data from 2007 to 2020, which was collected by the Geo-Informatics and Space Technology Development Agency of Thailand.

Suggested Citation

  • Dawud Thongtha & Nathakhun Wiroonsri, 2023. "Normal Approximation for Fire Incident Simulation Using Permanental Cox Processes," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-20, March.
  • Handle: RePEc:spr:metcap:v:25:y:2023:i:1:d:10.1007_s11009-023-10004-7
    DOI: 10.1007/s11009-023-10004-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11009-023-10004-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11009-023-10004-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Caroline Keef & Jonathan A. Tawn & Rob Lamb, 2013. "Estimating the probability of widespread flood events," Environmetrics, John Wiley & Sons, Ltd., vol. 24(1), pages 13-21, February.
    2. Mathias Bärtl & Simone Krummaker, 2020. "Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques," Risks, MDPI, vol. 8(1), pages 1-27, March.
    3. Andrea Gabrielli & Mario V. Wüthrich, 2018. "An Individual Claims History Simulation Machine," Risks, MDPI, vol. 6(2), pages 1-32, March.
    4. Anders Jessen & Thomas Mikosch & Gennady Samorodnitsky, 2011. "Prediction of outstanding payments in a Poisson cluster model," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2011(3), pages 214-237.
    5. J. Yang & K. Miescke & P. McCullagh, 2012. "Classification based on a permanental process with cyclic approximation," Biometrika, Biometrika Trust, vol. 99(4), pages 775-786.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zan Yu & Lianzeng Zhang, 2024. "Computing the Gerber-Shiu function with interest and a constant dividend barrier by physics-informed neural networks," Papers 2401.04378, arXiv.org.
    2. Benjamin Avanzi & Gregory Clive Taylor & Melantha Wang & Bernard Wong, 2020. "SynthETIC: an individual insurance claim simulator with feature control," Papers 2008.05693, arXiv.org, revised Aug 2021.
    3. Ihsan Chaoubi & Camille Besse & H'el`ene Cossette & Marie-Pier C^ot'e, 2022. "Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network," Papers 2201.13267, arXiv.org.
    4. Christopher Blier-Wong & Hélène Cossette & Luc Lamontagne & Etienne Marceau, 2020. "Machine Learning in P&C Insurance: A Review for Pricing and Reserving," Risks, MDPI, vol. 9(1), pages 1-26, December.
    5. Klaus Schneeberger & Matthias Huttenlau & Benjamin Winter & Thomas Steinberger & Stefan Achleitner & Johann Stötter, 2019. "A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 125-139, January.
    6. Evaggelia Siopi & Thomas Poufinas & James Ming Chen & Charalampos Agiropoulos, 2023. "Can Regulation Affect the Solvency of Insurers? New Evidence from European Insurers," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 29(1), pages 15-30, May.
    7. Gao, Guangyuan & Meng, Shengwang & Shi, Yanlin, 2021. "Dispersion modelling of outstanding claims with double Poisson regression models," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 572-586.
    8. Muhammed Taher Al-Mudafer & Benjamin Avanzi & Greg Taylor & Bernard Wong, 2021. "Stochastic loss reserving with mixture density neural networks," Papers 2108.07924, arXiv.org.
    9. Petr Suler & Zuzana Rowland & Tomas Krulicky, 2021. "Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China," JRFM, MDPI, vol. 14(2), pages 1-30, February.
    10. Benjamin Avanzi & Gregory Clive Taylor & Melantha Wang, 2021. "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator," Papers 2109.04058, arXiv.org, revised Mar 2022.
    11. Łukasz Delong & Mario V. Wüthrich, 2020. "Neural Networks for the Joint Development of Individual Payments and Claim Incurred," Risks, MDPI, vol. 8(2), pages 1-34, April.
    12. Valandis Elpidorou & Carolin Margraf & María Dolores Martínez-Miranda & Bent Nielsen, 2019. "A Likelihood Approach to Bornhuetter–Ferguson Analysis," Risks, MDPI, vol. 7(4), pages 1-20, December.
    13. Francesco Serinaldi & Chris G. Kilsby, 2017. "A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1958-1976, October.
    14. Vali Asimit & Ioannis Kyriakou & Jens Perch Nielsen, 2020. "Special Issue “Machine Learning in Insurance”," Risks, MDPI, vol. 8(2), pages 1-2, May.
    15. Avanzi, Benjamin & Taylor, Greg & Wang, Melantha & Wong, Bernard, 2021. "SynthETIC: An individual insurance claim simulator with feature control," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 296-308.
    16. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2020. "Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network," Papers 2008.07564, arXiv.org.
    17. B. Winter & K. Schneeberger & M. Huttenlau & J. Stötter, 2018. "Sources of uncertainty in a probabilistic flood risk model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(2), pages 431-446, March.
    18. H. Moel & B. Jongman & H. Kreibich & B. Merz & E. Penning-Rowsell & P. Ward, 2015. "Flood risk assessments at different spatial scales," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 20(6), pages 865-890, August.
    19. Yang Qiao & Chou-Wen Wang & Wenjun Zhu, 2024. "Machine learning in long-term mortality forecasting," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 340-362, April.
    20. Kevin Kuo, 2019. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Risks, MDPI, vol. 7(3), pages 1-12, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:metcap:v:25:y:2023:i:1:d:10.1007_s11009-023-10004-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.