CASdatasets usage in research articles and books

This vignette lists the published papers or books using datasets of CASdatasets package. References are ordered chronologically.

General usage and/or review papers

[1] P. Embrechts and M. V. Wüthrich. “Recent challenges in actuarial science”. In: Annual Review of Statistics and Its Application 9 (2022), pp. 119–140. DOI: 10.1146/annurev-statistics-040120-030244.

[2] A. Mashrur, W. Luo, N. A. Zaidi, et al. “Machine learning for financial risk management: a survey”. In: Ieee Access 8 (2020), pp. 203203–203223. DOI: 10.1109/ACCESS.2020.3036322.

[3] M. V. Wüthrich and M. Merz. Statistical foundations of actuarial learning and its applications. Springer Nature, 2023. DOI: 10.1007/978-3-031-12409-9.pdf.

Claim severity modeling

[1] H. Alsuhabi. “The new Topp-Leone exponentied exponential model for modeling financial data”. In: Mathematical Modelling and Control 4.1 (2024), p. 44. DOI: 10.3934/mmc.2024005.

[2] S. A. Bakar and S. Nadarajah. “Composite models with underlying folded distributions”. In: Journal of Computational and Applied Mathematics 390 (2021), p. 113351. DOI: 10.1016/j.cam.2020.113351.

[3] A. Chaturvedi, S. R. Bapat, and N. Joshi. “Sequential estimation of an inverse Gaussian mean with known coefficient of variation”. In: Sankhya B 84.1 (2022), pp. 402–420. DOI: 10.1007/s13571-021-00266-x.

[4] D. Chevalier and M. Côté. “From point to probabilistic gradient boosting for claim frequency and severity prediction”. In: European Actuarial Journal 15.3 (2025), pp. 707–752. DOI: 10.1007/s13385-025-00428-5.

[5] S. Ghaddab, M. Kacem, C. de Peretti, et al. “Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty”. In: Empirical Economics 65.3 (2023), pp. 1105–1127. DOI: 10.1007/s00181-023-02371-4.

[6] F. Holvoet, K. Antonio, and R. Henckaerts. “Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff”. In: North American Actuarial Journal 29.3 (2025), pp. 519–562. DOI: 10.1080/10920277.2025.2451860.

[7] M. A. Meraou, N. M. Al-Kandari, M. Z. Raqab, et al. “Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims”. In: Journal of Statistical Computation and Simulation 92.5 (2022), pp. 928–956. DOI: 10.1080/00949655.2021.1981324.

[8] L. Möstel, M. Fischer, and M. Pfeuffer. “Composite Tukey-type distributions with application to operational risk management”. In: Journal of Operational Risk (2024). DOI: 10.21314/JOP.2023.010.

[9] G. Pittarello, M. Hiabu, and A. M. Villegas. “Replicating and extending chain-ladder via an age–period–cohort structure on the claim development in a run-off triangle”. In: North American Actuarial Journal 30.1 (2026), pp. 1–31. DOI: 10.1080/10920277.2025.2496725.

[10] N. Počuča, P. Jevtić, P. D. McNicholas, et al. “Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models”. In: Insurance: Mathematics and Economics 94 (2020), pp. 79–93. DOI: 10.1016/j.insmatheco.2020.06.004.

[11] A. Punzo. “A new look at the inverse Gaussian distribution with applications to insurance and economic data”. In: Journal of Applied Statistics 46.7 (2019), pp. 1260–1287. DOI: 10.1080/02664763.2018.1542668. eprint: https://doi.org/10.1080/02664763.2018.1542668. URL: https://doi.org/10.1080/02664763.2018.1542668.

[12] M. Qazvini. “On the validation of claims with excess zeros in liability insurance: A comparative study”. In: Risks 7.3 (2019), p. 71. DOI: 10.3390/risks7030071.

[13] M. Raschke. “Alternative modelling and inference methods for claim size distributions”. In: Annals of Actuarial Science 14.1 (2020), pp. 1–19. DOI: 10.1017/S1748499519000010.

[14] S. D. Tomarchio, A. Punzo, J. T. Ferreira, et al. “Mode mixture of unimodal distributions for insurance loss data”. In: Annals of Operations Research (2024), pp. 1–19. DOI: 10.1007/s10479-024-06063-9.

Claim frequency modeling

[1] D. Chevalier and M. Côté. “From point to probabilistic gradient boosting for claim frequency and severity prediction”. In: European Actuarial Journal 15.3 (2025), pp. 707–752. DOI: 10.1007/s13385-025-00428-5.

[2] Ł. Delong, M. Lindholm, and M. V. Wüthrich. “Making Tweedie’s compound Poisson model more accessible”. In: European Actuarial Journal 11.1 (2021), pp. 185–226. DOI: 10.1007/s13385-021-00264-3.

[3] F. Holvoet, K. Antonio, and R. Henckaerts. “Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff”. In: North American Actuarial Journal 29.3 (2025), pp. 519–562. DOI: 10.1080/10920277.2025.2451860.

[4] Y. Liu, W. Li, and X. Zhang. “A marginalized zero-truncated Poisson regression model and its model averaging prediction: Y. Liu et al.” In: Communications in Mathematics and Statistics 13.3 (2025), pp. 527–570. DOI: 10.1007/s40304-022-00312-8.

[5] M. A. Meraou, N. M. Al-Kandari, M. Z. Raqab, et al. “Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims”. In: Journal of Statistical Computation and Simulation 92.5 (2022), pp. 928–956. DOI: 10.1080/00949655.2021.1981324.

[6] M. A. Meraou, M. Z. Raqab, and F. B. Almathkour. “Analyzing insurance data with an alpha power transformed exponential Poisson model”. In: Annals of Data Science 12.3 (2025), pp. 991–1011. DOI: 10.1007/s40745-024-00554-z.

[7] J. Merupula, V. Vaidyanathan, and C. Chesneau. “Prediction Interval for Compound Conway–Maxwell–Poisson Regression Model with Application to Vehicle Insurance Claim Data”. In: Mathematical and Computational Applications 28.2 (2023), p. 39. DOI: 10.3390/mca28020039.

[8] N. Počuča, P. Jevtić, P. D. McNicholas, et al. “Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models”. In: Insurance: Mathematics and Economics 94 (2020), pp. 79–93. DOI: 10.1016/j.insmatheco.2020.06.004.

[9] G. Willame, J. Trufin, and M. Denuit. “Boosted Poisson regression trees: a guide to the BT package in R”. In: Annals of Actuarial Science 18.3 (2024), pp. 605–625. DOI: 10.1017/S174849952300026X.

Risk measure

[1] A. G. Abubakari. “Actuarial measures, regression, and applications of exponentiated Fréchet loss distribution”. In: International Journal of Mathematics and Mathematical Sciences 2022.1 (2022), p. 3155188. DOI: 10.1155/2022/3155188.

[2] A. Y. Bin-Nun, C. Lizarazo, A. Panasci, et al. “What do surrogate safety metrics measure? Understanding driving safety as a continuum”. In: Accident Analysis & Prevention 195 (2024), p. 107245. DOI: 10.1016/j.aap.2023.107245.

[3] Y. Guan, Z. Jiao, and R. Wang. “A reverse ES (CVaR) optimization formula”. In: North American Actuarial Journal 28.3 (2024), pp. 611–625. DOI: 10.1080/10920277.2023.2249524.

[4] A. Staino, E. Russo, M. Costabile, et al. “Minimum capital requirement and portfolio allocation for non-life insurance: a semiparametric model with Conditional Value-at-Risk (CVaR) constraint: A. Staino et al.” In: Computational Management Science 20.1 (2023), p. 12. DOI: 10.1007/s10287-023-00439-1.

Pricing insurance

[1] A. Brauer. “Enhancing actuarial non-life pricing models via transformers”. In: European Actuarial Journal 14.3 (2024), pp. 991–1012. DOI: 10.1007/s13385-024-00388-2.

[2] Ł. Delong, M. Lindholm, and M. V. Wüthrich. “Making Tweedie’s compound Poisson model more accessible”. In: European Actuarial Journal 11.1 (2021), pp. 185–226. DOI: 10.1007/s13385-021-00264-3.

[3] F. Holvoet, K. Antonio, and R. Henckaerts. “Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff”. In: North American Actuarial Journal 29.3 (2025), pp. 519–562. DOI: 10.1080/10920277.2025.2451860.

[4] M. Lindholm, F. Lindskog, and J. Palmquist. “Local bias adjustment, duration-weighted probabilities, and automatic construction of tariff cells”. In: Scandinavian Actuarial Journal 2023.10 (2023), pp. 946–973. DOI: 10.1080/03461238.2023.2176251.

[5] M. Lindholm and T. Nazar. “On duration effects in non-life insurance pricing”. In: European Actuarial Journal 14.3 (2024), pp. 809–832. DOI: 10.1007/s13385-024-00385-5.

[6] M. Lindholm and J. Palmquist. “Black-box guided generalised linear model building with non-life pricing applications”. In: Annals of Actuarial Science 18.3 (2024), pp. 675–691. DOI: 10.1017/S1748499524000265.

[7] M. A. Meraou, N. M. Al-Kandari, M. Z. Raqab, et al. “Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims”. In: Journal of Statistical Computation and Simulation 92.5 (2022), pp. 928–956. DOI: 10.1080/00949655.2021.1981324.

[8] M. Meraou, N. Al-Kandari, and M. Raqab. “Univariate and bivariate compound models based on random sum of variates with application to the insurance losses data”. In: Journal of Statistical Theory and Practice 16.4 (2022), p. 56. DOI: 10.1007/s42519-022-00282-8.

[9] R. Wang, H. Shi, and J. Cao. “A Nested GLM Framework with Neural Network Encoding and Spatially Constrained Clustering in Non-Life Insurance Ratemaking”. In: North American Actuarial Journal 29.3 (2025), pp. 645–661. DOI: 10.1080/10920277.2024.2442416.

[10] X. Xin and F. Huang. “Antidiscrimination insurance pricing: Regulations, fairness criteria, and models”. In: North American Actuarial Journal 28.2 (2024), pp. 285–319. DOI: 10.1080/10920277.2023.2190528.

Extreme value analysis

[1] M. Allouche, J. El Methni, and S. Girard. “A refined Weissman estimator for extreme quantiles”. In: Extremes 26.3 (2023), pp. 545–572. DOI: 10.1007/s10687-022-00452-8.

[2] S. Girard, G. Stupfler, and A. Usseglio-Carleve. “On automatic bias reduction for extreme expectile estimation”. In: Statistics and Computing 32.4 (2022), p. 64. DOI: 10.1007/s11222-022-10118-x.

[3] J. Meng and K. Chan. “Penalized quasi-likelihood estimation of generalized Pareto regression–consistent identification of risk factors for extreme losses”. In: Insurance: Mathematics and Economics 104 (2022), pp. 60–75. DOI: 10.1016/j.insmatheco.2022.01.005.

[4] G. Stupfler and A. Usseglio-Carleve. “Composite bias-reduced L p-quantile-based estimators of extreme quantiles and expectiles”. In: Canadian Journal of Statistics 51.2 (2023), pp. 704–742. DOI: 10.1002/cjs.11703.

Multivariate and copula models

[1] A. Brouste, C. Dutang, L. Hovsepyan, et al. “Fast inference in copula models with categorical explanatory variables using the one-step procedure”. In: Computational Statistics 41.1 (2026), p. 23. DOI: 10.1007/s00180-025-01692-5.

[2] N. W. Deresa, I. Van Keilegom, and K. Antonio. “Copula-based inference for bivariate survival data with left truncation and dependent censoring”. In: Insurance: Mathematics and Economics 107 (2022), pp. 1–21. DOI: 10.1016/j.insmatheco.2022.07.011.

[3] Q. Hoang, P. Khandelwal, and S. Ghosh. “Robust predictive model using copulas”. In: Data-Enabled Discovery and Applications 3.1 (2019), p. 8. DOI: 10.1007/s41688-019-0032-y.

[4] M. Meraou, N. Al-Kandari, and M. Raqab. “Univariate and bivariate compound models based on random sum of variates with application to the insurance losses data”. In: Journal of Statistical Theory and Practice 16.4 (2022), p. 56. DOI: 10.1007/s42519-022-00282-8.

[5] S. F. Syed Yusoff Alhabshi, Z. H. Zamzuri, and S. N. Mohd Ramli. “Monte carlo simulation of the moments of a copula-dependent risk process with weibull interwaiting time”. In: Risks 9.6 (2021), p. 109. DOI: 10.3390/risks9060109.

Bayesian analysis

[1] P. Goffard and P. J. Laub. “Approximate Bayesian Computations to fit and compare insurance loss models”. In: Insurance: Mathematics and Economics 100 (2021), pp. 350–371. DOI: 10.1016/j.insmatheco.2021.06.002.

[2] F. Ungolo and E. R. van den Heuvel. “A Dirichlet process mixture regression model for the analysis of competing risk events”. In: Insurance: Mathematics and Economics 116 (2024), pp. 95–113. DOI: 10.1016/j.insmatheco.2024.02.004.

Other topics

[1] H. M. Aljohani. “Statistical inference for a novel distribution using ranked set sampling with applications”. In: Heliyon 10.5 (2024). DOI: 10.1016/j.heliyon.2024.e26893.

[2] B. Avanzi, E. Dong, P. J. Laub, et al. “Distributional refinement network: Distributional forecasting via deep learning”. In: Insurance: Mathematics and Economics (2026), p. 103246. DOI: 10.1016/j.insmatheco.2026.103246.

[3] B. Avanzi, G. Taylor, and M. Wang. “SPLICE: a synthetic paid loss and incurred cost experience simulator”. In: Annals of Actuarial Science 17.1 (2023), pp. 7–35. DOI: 10.1017/S1748499522000057.

[4] M. Bladt and C. B. Gardner. “Joint discrete and continuous matrix distribution modeling”. In: Stochastic Models 40.1 (2024), pp. 1–37. DOI: 10.1080/15326349.2023.2185257.

[5] A. Brouste, C. Dutang, and T. Rohmer. “A closed-form alternative estimator for GLM with categorical explanatory variables”. In: Communications in Statistics-Simulation and Computation 53.5 (2024), pp. 2444–2460. DOI: 10.1080/03610918.2022.2076870.

[6] R. Henckaerts, K. Antonio, and M. Côté. “When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates”. In: Expert Systems with Applications 202 (2022), p. 117230. DOI: 10.1016/j.eswa.2022.117230.

[7] D. Lim, A. Neufeld, S. Sabanis, et al. “Langevin dynamics based algorithm e-TH \(\varepsilon\) O POULA for stochastic optimization problems with discontinuous stochastic gradient”. In: Mathematics of Operations Research 50.3 (2025), pp. 2333–2374. DOI: 10.1287/moor.2022.0307.

[8] A. Majeed. “Accelerated failure time models: An application in insurance attrition”. In: The Journal of Risk Management and Insurance (2020).

[9] T. Miljkovic and D. Fernández. “On two mixture-based clustering approaches used in modeling an insurance portfolio”. In: Risks 6.2 (2018), p. 57. DOI: 10.3390/risks6020057.

[10] T. Miljkovic and P. Wang. “A dimension reduction assisted credit scoring method for big data with categorical features”. In: Financial Innovation 11.1 (2025), p. 29. DOI: 10.1186/s40854-024-00689-1.

[11] J. Ponnet, J. Raymaekers, and T. Verdonck. “Fast thresholded concordance probability for evolutionary optimization”. In: Swarm and Evolutionary Computation 78 (2023), p. 101260. DOI: 10.1016/j.swevo.2023.101260.

[12] R. Richman and M. V. Wüthrich. “LocalGLMnet: interpretable deep learning for tabular data”. In: Scandinavian Actuarial Journal 2023.1 (2023), pp. 71–95. DOI: 10.1080/03461238.2022.2081816.

[13] R. Richman and M. V. Wüthrich. “Nagging predictors”. In: Risks 8.3 (2020), p. 83. DOI: 10.3390/risks8030083.

[14] P. Shi and K. Shi. “Non-life insurance risk classification using categorical embedding”. In: North American Actuarial Journal 27.3 (2023), pp. 579–601. DOI: 10.1080/10920277.2022.2123361.

[15] S. C. Tseung, A. L. Badescu, T. C. Fung, et al. “LRMoE.jl: a software package for insurance loss modelling using mixture of experts regression model”. In: Annals of Actuarial Science 15.2 (2021), pp. 419–440. DOI: 10.1017/S1748499521000087.

[16] M. V. Wüthrich and J. Ziegel. “Isotonic recalibration under a low signal-to-noise ratio”. In: Scandinavian Actuarial Journal 2024.3 (2024), pp. 279–299. DOI: 10.1080/03461238.2023.2246743.