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Multivariate Modeling of Precipitation-Induced Home Insurance Risks Using Data Depth

Author

Listed:
  • Asim K. Dey

    (University of Texas at El Paso
    Princeton University)

  • Vyacheslav Lyubchich

    (University of Maryland Center for Environmental Science)

  • Yulia R. Gel

    (National Science Foundation)

Abstract

While political debates on climate change become increasingly heated, our houses and city infrastructure continue to suffer from an increasing trend of damages due to adverse atmospheric events, from heavier-than-usual rainfalls to heat waves, droughts, and floods. Adapting our homes and critical infrastructure to sustain the effects of climate dynamics requires novel data-driven interdisciplinary approaches for efficient risk mitigation. We develop a new systematic framework based on the machinery of statistical and machine learning tools to evaluate water-related home insurance risks and quantify uncertainty due to varying climate model projections. Furthermore, we introduce the concept of data depth to the analysis of weather and climate ensembles, which remains a novel territory for statistical depth methodology as well as the field of environmental risk and ensemble forecasting in general. We illustrate the new data-driven methodology for risk analysis in application to rainfall-related home insurance in the Canadian Prairies over 2002–2011. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Asim K. Dey & Vyacheslav Lyubchich & Yulia R. Gel, 2024. "Multivariate Modeling of Precipitation-Induced Home Insurance Risks Using Data Depth," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(1), pages 36-55, March.
    • Handle: RePEc:spr:jagbes:v:29:y:2024:i:1:d:10.1007_s13253-023-00554-1
    DOI: 10.1007/s13253-023-00554-1
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    References listed on IDEAS

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