Normal Approximation for Fire Incident Simulation Using Permanental Cox Processes
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DOI: 10.1007/s11009-023-10004-7
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- 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.
- Andrea Gabrielli & Mario V. Wüthrich, 2018. "An Individual Claims History Simulation Machine," Risks, MDPI, vol. 6(2), pages 1-32, March.
- 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.
- 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.
- 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.
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Keywords
Correlation inequality; Cox process; Local dependence; Random fields; Natural disaster; Positive association;All these keywords.
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