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A zero-inflated mixed-effects spatial point process for grouped storm loss data

  • Lisa Gao
  • Sébastien Jessup
  • Tianxing Yan
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Abstract

The increasing granularity of third-party weather and exposure information can allow insurers to more effectively predict weather-related losses. However, loss outcomes are often reported in spatially grouped observations, such as at the county level, so higher resolution predictors are aggregated to align with the granularity of the outcome in standard analyses. Assuming an underlying zero-inflated mixed-effects spatial point process framework for claims arising from a common storm, we derive a model for unbalanced, multivariate zero-inflated count data that incorporates rich weather and exposure predictors observed at higher spatial granularity to predict claim patterns. The model accommodates the dependence between locations affected by a common storm in the excess zeros, as well as in the joint claim counts. Using real property exposure and loss data, we emphasize the value of incorporating granular predictors to address the localized heterogeneity of storm losses.

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Cite

BibTeXRIS
BibTeX
@misc{gao2026zeroinflated,
  title = {{A zero-inflated mixed-effects spatial point process for grouped storm loss data}},
  author = {Lisa Gao and Sébastien Jessup and Tianxing Yan},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2607.03852},
  eprint = {2607.03852},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.03852},
}
RIS
TY  - UNPB
TI  - A zero-inflated mixed-effects spatial point process for grouped storm loss data
AU  - Lisa Gao
AU  - Sébastien Jessup
AU  - Tianxing Yan
PY  - 2026
DA  - 2026/09/21
PB  - arXiv
DO  - 10.48550/arxiv.2607.03852
UR  - https://arxiv.org/abs/2607.03852
AB  - The increasing granularity of third-party weather and exposure information can allow insurers to more effectively predict weather-related losses. However, loss outcomes are often reported in spatially grouped observations, such as at the county level, so higher resolution predictors are aggregated to align with the granularity of the outcome in standard analyses. Assuming an underlying zero-inflated mixed-effects spatial point process framework for claims arising from a common storm, we derive a model for unbalanced, multivariate zero-inflated count data that incorporates rich weather and exposure predictors observed at higher spatial granularity to predict claim patterns. The model accommodates the dependence between locations affected by a common storm in the excess zeros, as well as in the joint claim counts. Using real property exposure and loss data, we emphasize the value of incorporating granular predictors to address the localized heterogeneity of storm losses.
ER  -