Tsz Chai Fung, George Tzougas, Mario Wuthrich
arXiv 12 Mar 2021 · Statistics — Methodology · publishedNorth American Actuarial Journal (2022) · 4 citations (OpenAlex)
arXiv:2103.07200 · PDF · DOI · OpenAlex · Extracted main text
The aim of this paper is to present a mixture composite regression model for claim severity modelling. Claim severity modelling poses several challenges such as multimodality, heavy-tailedness and systematic effects in data. We tackle this modelling problem by studying a mixture composite regression model for simultaneous modeling of attritional and large claims, and for considering systematic effects in both the mixture components as well as the mixing probabilities. For model fitting, we present a group-fused regularization approach that allows us for selecting the explanatory variables which significantly impact the mixing probabilities and the different mixture components, respectively. We develop an asymptotic theory for this regularized estimation approach, and fitting is performed using a novel Generalized Expectation-Maximization algorithm. We exemplify our approach on real motor insurance data set.
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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. Devriendt, K. Antonio, T. Reynkens, and R. Verbelen (2020) Sparse regression with multi-type regularized feature modeling | 1.000 | 5 | 3 | 100% |
| 2 | J. Fan and R. Li (2001) Variable selection via nonconcave penalized likelihood and its oracle properties | 0.941 | 6 | 6 | 83% |
| 3 | T. Reynkens, R. Verbelen, J. Beirlant, and K. Antonio (2017) Modelling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions | 0.928 | 4 | 3 | 100% |
| 4 | M.-R. Oelker and G. Tutz (2017) A uniform framework for the combination of penalties in generalized structured models | 0.811 | 4 | 2 | 100% |
| 5 | M. Blostein and T. Miljkovic (2019) On modeling left-truncated loss data using mixtures of distributions | 0.644 | 2 | 2 | 100% |
| 6 | T. C. Fung, A. L. Badescu, and X. S. Lin (2020) Fitting censored and truncated regression data using the mixture of experts models self | 0.585 | 3 | 1 | 100% |
| 7 | A. Khalili (2010) New estimation and feature selection methods in mixture-of-experts models | 0.481 | 6 | 2 | 17% |
| 8 | 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 | 0.405 | 1 | 1 | 100% |
| 9 | T. C. Fung, A. L. Badescu, and X. S. Lin (2020) A new class of severity regression models with an application to ibnr prediction self | 0.405 | 1 | 1 | 100% |
| 10 | S. C. Lee and X. S. Lin (2010) Modeling and evaluating insurance losses via mixtures of Erlang distributions | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 30 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Posteriori Risk Classification and Ratemaking with Random Effects in the Mixture-of-Experts Model | 0.405 | 1 | 1 |