Statistical Foundations of Actuarial Learning and its Applications
Open accessAbstract
This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis, model building.
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Authors such as Wüthrich and Buser (2023) and Wüthrich and Merz (2023) suggest using Poisson deviance and Gamma deviance as appropriate loss functions in GBM models, a methodological option we pursued.
“Because of this, authors such as Wüthrich and Buser (2023) and Wüthrich and Merz (2023) suggest using Poisson deviance and Gamma deviance as an appropriate loss function in GBMmodels, a methodological option we pursued in this paper.”
From Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting (2023) · cites this paper as Wüthrich and Merz (2023)
Chapter 4 (pp. 90–91) treats deviance as an appropriate loss for mean prediction, and Remark 5.14 (pp. 132–133) describes boosting, noting that it is typically applied to regression trees. The passages do not specifically suggest using both Poisson and Gamma deviance as loss functions in GBM models; they establish the loss functions and boosting separately. (LLM, conf=0.82)
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Cites
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417 · see citing works
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [371]371. Verbelen, R., Gong, L., Antonio, K., Badescu, A., & Lin, S. (2015). Fitting mixtures of Erlangs to censored and truncated data using the EM algorithm. ASTIN Bulletin, 45/3, 729–
- [758]758. 372. Verschuren, R. M. (2021). Predictive claim scores for dynamic multi-product risk classifica- tion in insurance. ASTIN Bulletin, 51/1, 1–25.
- [373]373. Wager, S., Wang, S., & Liang, P. S. (2013). Dropout training as adaptive regularization. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Weinberger (Eds.), Advances in neural information processing systems (Vol. 26, pp. 351–359). Red Hook: Curran Associates.
- [374]374. Wald, A. (1949). Note on the consistency of the maximum likelihood estimate. Annals of Mathematical Statistics, 20/4, 595–601.
- [375]375. Wang, C.-W., Zhang, J., & Zhu, W. (2021). Neighbouring prediction for mortality. ASTIN Bulletin, 51/3, 689–718.
Cited by
Sorted by citations
Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
Clemente, C., Guerreiro, G. R., & Bravo, J. M. (2023) · Risks · doi:10.3390/risks11090163 · Cited by 28 · see citing works
Sensitivity-based measures of discrimination in insurance pricing
Lindholm, M., Richman, R., Tsanakas, A., & Wüthrich, M. V. (2026) · European Journal of Operational Research · doi:10.1016/j.ejor.2026.01.021 · Cited by 1 · see citing works
Self-citation
- Authors
- Wüthrich, M. V., & Merz, M.
- Affiliations
- ETH Zurich (CH)
- Universität Hamburg (DE)
- Venue
- Springer Actuarial
- Publisher
- Springer International Publishing
- Published
- 2023
- ISSN
- 2523-3262 (ISSN-L)
- 2523-3270
- OpenAlex
- W4309645569
- Cited by
- 95
- References
- 3
- Citations by year
- 2026: 14 · 2025: 20 · 2024: 30 · 2023: 26 · 2022: 2 · 2021: 1 · 2017: 1 · 2013: 1
- FWCI
- 15.88
- Type
- book
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Springer Actuarial (publishedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Probability and Risk Models
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
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