Spark C. Tseung, Ian Weng Chan, Tsz Chai Fung, Andrei L. Badescu, X. Sheldon Lin
arXiv 30 Sep 2022 · Statistics — Applications
arXiv:2209.15212 · PDF · DOI · OpenAlex · Extracted main text
A well-designed framework for risk classification and ratemaking in automobile insurance is key to insurers' profitability and risk management, while also ensuring that policyholders are charged a fair premium according to their risk profile. In this paper, we propose to adapt a flexible regression model, called the Mixed LRMoE, to the problem of a posteriori risk classification and ratemaking, where policyholder-level random effects are incorporated to better infer their risk profile reflected by the claim history. We also develop a stochastic variational Expectation-Conditional-Maximization algorithm for estimating model parameters and inferring the posterior distribution of random effects, which is numerically efficient and scalable to large insurance portfolios. We then apply the Mixed LRMoE model to a real, multiyear automobile insurance dataset, where the proposed framework is shown to offer better fit to data and produce posterior premium which accurately reflects policyholders' claim history.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | S.-K. Ng and G. J. McLachlan (2007) Extension of mixture-of-experts networks for binary classification of hierarchical data | 0.928 | 4 | 3 | 100% |
| 2 | K. K. Yau, A. H. Lee, and A. S. Ng (2003) Finite mixture regression model with random effects: Application to neonatal hospital length of stay | 0.928 | 4 | 3 | 100% |
| 3 | T. C. Fung, S. C. Tseung, A. L. Badescu, and X. S. Lin (2022) Mixture of experts models for multilevel data: Modelling framework and approximation theory self | 0.874 | 5 | 2 | 100% |
| 4 | T. C. Fung, A. L. Badescu, and X. S. Lin (2019) A class of mixture of experts models for general insurance: Theoretical developments self | 0.811 | 4 | 2 | 100% |
| 5 | T. C. Fung, A. L. Badescu, and X. S. Lin (2019) A class of mixture of experts models for general insurance: Application to correlated claim frequencies self | 0.811 | 4 | 2 | 100% |
| 6 | D. M. Blei, A. Kucukelbir, and J. D. McAuliffe (2017) Variational inference: A review for statisticians | 0.737 | 3 | 2 | 100% |
| 7 | M. I. Jordan and R. A. Jacobs (1994) Hierarchical mixtures of experts and the EM algorithm | 0.644 | 2 | 2 | 100% |
| 8 | S.-K. Ng and G. J. McLachlan (2014) Mixture models for clustering multilevel growth trajectories | 0.644 | 2 | 2 | 100% |
| 9 | S. C. Tseung, A. L. Badescu, T. C. Fung, and X. S. Lin (2021) LRMoE. jl: a software package for insurance loss modelling using mixture of experts regression model self | 0.644 | 2 | 2 | 100% |
| 10 | G. Tzougas and A. P. di Cerchiara (2021) The multivariate mixed negative binomial regression model with an application to insurance a posteriori ratemaking | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 58 scored citations.