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Flexible (panel) regression models for bivariate count–continuous data with an insurance application

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  • Yang Lu

Abstract

We propose a flexible regression model that is suitable for mixed count–continuous panel data. The model is based on a compound Poisson representation of the continuous variable, with bivariate random effect following a polynomial‐expansion‐based joint density. Besides the distributional flexibility that it offers, the model allows for closed form forecast updating formulae. This property is especially important for insurance applications, in which the future individual insurance premium should be regularly updated according to one's own past claim history. An application to vehicle insurance claims is provided.

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  • Yang Lu, 2019. "Flexible (panel) regression models for bivariate count–continuous data with an insurance application," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1503-1521, October.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:4:p:1503-1521
    DOI: 10.1111/rssa.12470
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    1. Verschuren, Robert Matthijs, 2022. "Frequency-severity experience rating based on latent Markovian risk profiles," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 379-392.
    2. Cheung, Eric C.K. & Ni, Weihong & Oh, Rosy & Woo, Jae-Kyung, 2021. "Bayesian credibility under a bivariate prior on the frequency and the severity of claims," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 274-295.
    3. Denuit, Michel & Lu, Yang, 2020. "Wishart-Gamma mixtures for multiperil experience ratemaking, frequency-severity experience rating and micro-loss reserving," LIDAM Discussion Papers ISBA 2020016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Michel Denuit & Yang Lu, 2021. "Wishart‐gamma random effects models with applications to nonlife insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(2), pages 443-481, June.

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