This vignette lists the published papers or books using datasets of CASdatasets package.
General usage and/or review papers
Mashrur A, Luo W, Zaidi NA, Robles-Kelly A (2020). “Machine learning for financial risk management: a survey.” Ieee Access, 8, 203203–203223.
Embrechts P, Wüthrich MV (2022). “Recent challenges in actuarial science.” Annual Review of Statistics and Its Application, 9, 119–140.
Wüthrich MV, Merz M (2023). Statistical foundations of actuarial learning and its applications. Springer Nature.
Claim severity modeling
Qazvini M (2019). “On the validation of claims with excess zeros in liability insurance: A comparative study.” Risks, 7(3), 71.
Punzo A (2019). “A new look at the inverse Gaussian distribution with applications to insurance and economic data.” Journal of Applied Statistics, 46(7), 1260–1287.
Počuča N, Jevtić P, McNicholas PD, Miljkovic T (2020). “Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models.” Insurance: Mathematics and Economics, 94, 79–93.
Raschke M (2020). “Alternative modelling and inference methods for claim size distributions.” Annals of Actuarial Science, 14(1), 1–19.
Bakar SA, Nadarajah S (2021). “Composite models with underlying folded distributions.” Journal of Computational and Applied Mathematics, 390, 113351.
Meraou MA, Al-Kandari NM, Raqab MZ, Kundu D (2022). “Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims.” Journal of Statistical Computation and Simulation, 92(5), 928–956.
Chaturvedi A, Bapat SR, Joshi N (2022). “Sequential estimation of an inverse Gaussian mean with known coefficient of variation.” Sankhya B, 84(1), 402–420.
Ghaddab S, Kacem M, de Peretti C, Belkacem L (2023). “Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty.” Empirical Economics, 65(3), 1105–1127.
Tomarchio SD, Punzo A, Ferreira JT, Bekker A (2024). “Mode mixture of unimodal distributions for insurance loss data.” Annals of Operations Research, 1–19.
Möstel L, Fischer M, Pfeuffer M (2024). “Composite Tukey-type distributions with application to operational risk management.” Journal of Operational Risk.
Alsuhabi H (2024). “The new Topp-Leone exponentied exponential model for modeling financial data.” Mathematical Modelling and Control, 4(1), 44.
Chevalier D, Côté M (2025). “From point to probabilistic gradient boosting for claim frequency and severity prediction.” European Actuarial Journal, 15(3), 707–752.
Holvoet F, Antonio K, Henckaerts R (2025). “Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff.” North American Actuarial Journal, 29(3), 519–562.
Pittarello G, Hiabu M, Villegas AM (2026). “Replicating and extending chain-ladder via an age–period–cohort structure on the claim development in a run-off triangle.” North American Actuarial Journal, 30(1), 1–31.
Claim frequency modeling
Počuča N, Jevtić P, McNicholas PD, Miljkovic T (2020). “Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models.” Insurance: Mathematics and Economics, 94, 79–93.
Delong Ł, Lindholm M, Wüthrich MV (2021). “Making Tweedie’s compound Poisson model more accessible.” European Actuarial Journal, 11(1), 185–226. doi:10.1007/s13385-021-00264-3, https://doi.org/10.1007/s13385-021-00264-3.
Meraou MA, Al-Kandari NM, Raqab MZ, Kundu D (2022). “Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims.” Journal of Statistical Computation and Simulation, 92(5), 928–956.
Merupula J, Vaidyanathan VS, Chesneau C (2023). “Prediction Interval for Compound Conway–Maxwell–Poisson Regression Model with Application to Vehicle Insurance Claim Data.” Mathematical and Computational Applications, 28(2), 39. doi:10.3390/mca28020039, https://doi.org/10.3390/mca28020039.
Willame G, Trufin J, Denuit M (2024). “Boosted Poisson regression trees: a guide to the BT package in R.” Annals of Actuarial Science, 18(3), 605–625.
Liu Y, Li W, Zhang X (2025). “A marginalized zero-truncated Poisson regression model and its model averaging prediction: Y. Liu et al.” Communications in Mathematics and Statistics, 13(3), 527–570.
Holvoet F, Antonio K, Henckaerts R (2025). “Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff.” North American Actuarial Journal, 29(3), 519–562.
Chevalier D, Côté M (2025). “From point to probabilistic gradient boosting for claim frequency and severity prediction.” European Actuarial Journal, 15(3), 707–752.
Meraou MA, Raqab MZ, Almathkour FB (2025). “Analyzing insurance data with an alpha power transformed exponential Poisson model.” Annals of Data Science, 12(3), 991–1011.
Risk measure
Abubakari AG (2022). “Actuarial measures, regression, and applications of exponentiated Fréchet loss distribution.” International Journal of Mathematics and Mathematical Sciences, 2022(1), 3155188.
Staino A, Russo E, Costabile M, Leccadito A (2023). “Minimum capital requirement and portfolio allocation for non-life insurance: a semiparametric model with Conditional Value-at-Risk (CVaR) constraint: A. Staino et al.” Computational Management Science, 20(1), 12.
Guan Y, Jiao Z, Wang R (2024). “A reverse ES (CVaR) optimization formula.” North American Actuarial Journal, 28(3), 611–625.
Bin-Nun AY, Lizarazo C, Panasci A, Madden S, Tebbens RJD (2024). “What do surrogate safety metrics measure? Understanding driving safety as a continuum.” Accident Analysis & Prevention, 195, 107245.
Pricing insurance
Delong Ł, Lindholm M, Wüthrich MV (2021). “Making Tweedie’s compound Poisson model more accessible.” European Actuarial Journal, 11(1), 185–226. doi:10.1007/s13385-021-00264-3, https://doi.org/10.1007/s13385-021-00264-3.
Meraou MA, Al-Kandari NM, Raqab MZ, Kundu D (2022). “Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims.” Journal of Statistical Computation and Simulation, 92(5), 928–956.
Meraou MA, Al-Kandari NM, Raqab MZ (2022). “Univariate and bivariate compound models based on random sum of variates with application to the insurance losses data.” Journal of Statistical Theory and Practice, 16(4), 56.
Lindholm M, Lindskog F, Palmquist J (2023). “Local bias adjustment, duration-weighted probabilities, and automatic construction of tariff cells.” Scandinavian Actuarial Journal, 2023(10), 946–973.
Xin X, Huang F (2024). “Antidiscrimination insurance pricing: Regulations, fairness criteria, and models.” North American Actuarial Journal, 28(2), 285–319.
Lindholm M, Nazar T (2024). “On duration effects in non-life insurance pricing.” European Actuarial Journal, 14(3), 809–832.
Brauer A (2024). “Enhancing actuarial non-life pricing models via transformers.” European Actuarial Journal, 14(3), 991–1012.
Lindholm M, Palmquist J (2024). “Black-box guided generalised linear model building with non-life pricing applications.” Annals of Actuarial Science, 18(3), 675–691.
Wang R, Shi H, Cao J (2025). “A Nested GLM Framework with Neural Network Encoding and Spatially Constrained Clustering in Non-Life Insurance Ratemaking.” North American Actuarial Journal, 29(3), 645–661.
Holvoet F, Antonio K, Henckaerts R (2025). “Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff.” North American Actuarial Journal, 29(3), 519–562.
Extreme value analysis
Girard S, Stupfler G, Usseglio-Carleve A (2022). “On automatic bias reduction for extreme expectile estimation.” Statistics and Computing, 32(4), 64.
Meng J, Chan K (2022). “Penalized quasi-likelihood estimation of generalized Pareto regression–consistent identification of risk factors for extreme losses.” Insurance: Mathematics and Economics, 104, 60–75.
Allouche M, El Methni J, Girard S (2023). “A refined Weissman estimator for extreme quantiles.” Extremes, 26(3), 545–572.
Stupfler G, Usseglio-Carleve A (2023). “Composite bias-reduced L p-quantile-based estimators of extreme quantiles and expectiles.” Canadian Journal of Statistics, 51(2), 704–742.
Multivariate and copula models
Hoang Q, Khandelwal P, Ghosh S (2019). “Robust predictive model using copulas.” Data-Enabled Discovery and Applications, 3(1), 8.
Syed Yusoff Alhabshi SF, Zamzuri ZH, Mohd Ramli SN (2021). “Monte carlo simulation of the moments of a copula-dependent risk process with weibull interwaiting time.” Risks, 9(6), 109.
Deresa NW, Van Keilegom I, Antonio K (2022). “Copula-based inference for bivariate survival data with left truncation and dependent censoring.” Insurance: Mathematics and Economics, 107, 1–21.
Meraou MA, Al-Kandari NM, Raqab MZ (2022). “Univariate and bivariate compound models based on random sum of variates with application to the insurance losses data.” Journal of Statistical Theory and Practice, 16(4), 56.
Brouste A, Dutang C, Hovsepyan L, Rohmer T (2026). “Fast inference in copula models with categorical explanatory variables using the one-step procedure.” Computational Statistics, 41(1), 23.
Bayesian analysis
Goffard P, Laub PJ (2021). “Approximate Bayesian Computations to fit and compare insurance loss models.” Insurance: Mathematics and Economics, 100, 350–371.
Ungolo F, van den Heuvel ER (2024). “A Dirichlet process mixture regression model for the analysis of competing risk events.” Insurance: Mathematics and Economics, 116, 95–113.
Other topics
Miljkovic T, Fernández D (2018). “On two mixture-based clustering approaches used in modeling an insurance portfolio.” Risks, 6(2), 57.
Richman R, Wüthrich MV (2020). “Nagging predictors.” Risks, 8(3), 83.
Majeed A (2020). “Accelerated failure time models: An application in insurance attrition.” The Journal of Risk Management and Insurance.
Tseung SC, Badescu AL, Fung TC, Lin XS (2021). “LRMoE.jl: a software package for insurance loss modelling using mixture of experts regression model.” Annals of Actuarial Science, 15(2), 419–440.
Henckaerts R, Antonio K, Côté M (2022). “When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates.” Expert Systems with Applications, 202, 117230.
Shi P, Shi K (2023). “Non-life insurance risk classification using categorical embedding.” North American Actuarial Journal, 27(3), 579–601.
Avanzi B, Taylor G, Wang M (2023). “SPLICE: a synthetic paid loss and incurred cost experience simulator.” Annals of Actuarial Science, 17(1), 7–35.
Ponnet J, Raymaekers J, Verdonck T (2023). “Fast thresholded concordance probability for evolutionary optimization.” Swarm and Evolutionary Computation, 78, 101260.
Richman R, Wüthrich MV (2023). “LocalGLMnet: interpretable deep learning for tabular data.” Scandinavian Actuarial Journal, 2023(1), 71–95.
Brouste A, Dutang C, Rohmer T (2024). “A closed-form alternative estimator for GLM with categorical explanatory variables.” Communications in Statistics-Simulation and Computation, 53(5), 2444–2460.
Bladt M, Gardner CB (2024). “Joint discrete and continuous matrix distribution modeling.” Stochastic Models, 40(1), 1–37.
Wüthrich MV, Ziegel J (2024). “Isotonic recalibration under a low signal-to-noise ratio.” Scandinavian Actuarial Journal, 2024(3), 279–299.
Aljohani HM (2024). “Statistical inference for a novel distribution using ranked set sampling with applications.” Heliyon, 10(5).
Lim D, Neufeld A, Sabanis S, Zhang Y (2025). “Langevin dynamics based algorithm e-TH O POULA for stochastic optimization problems with discontinuous stochastic gradient.” Mathematics of Operations Research, 50(3), 2333–2374.
Miljkovic T, Wang P (2025). “A dimension reduction assisted credit scoring method for big data with categorical features.” Financial Innovation, 11(1), 29.
Avanzi B, Dong E, Laub PJ, Wong B (2026). “Distributional refinement network: Distributional forecasting via deep learning.” Insurance: Mathematics and Economics, 103246.