References
- Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. (1984). Classification and regression trees. Wadsworth Statistics/Probability Series: Belmont, CA.
- Delong, L., Lindholm, M. & Wüthrich, M. V. (2021). Making Tweedie's compound Poisson model more accessible. European Actuarial Journal 11, 185–226.
- Denuit, M., Hainaut, D. & Trufin, J. (2019a). Effective statistical learning methods for actuaries I: GLM and extensions. Switzerland, AG: Springer Nature.
- Denuit, M., Hainaut, D. & Trufin, J. (2019b). Effective statistical learning methods for actuaries III: neural networks and extensions. Switzerland, AG: Springer Nature.
- Denuit, M., Hainaut, D. & Trufin, J. (2020). Effective statistical learning methods for actuaries II: tree-based methods and extensions. Switzerland, AG: Springer Nature.
- Friedman, J. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics 29(5), 1189–1232.
- Friedman, J., Hastie, T. & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Annals of Statistics 28(2), 337–407.
- Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications 39, 3659–3667.
- Henckaerts, R. (2020). distRforest: distribution-based random forest. https://www.github.com/henckr/distRforest.
- Henckaerts, R., Cote, M-P., Antonio, K. & Verbelen, R. (2021). Boosting insights in insurance tariff plans with tree-based machine learning methods. North American Actuarial Journal 25, 255–285.
- Lee, S. C. (2020). Delta boosting implementation of negative binomial regression in actuarial pricing. Risks 8, 1–19.
- Lee, S. C. & Lin, S. (2018). Delta boosting machine with application to general insurance. North American Actuarial Journal 22, 405–425.
- Liu, Y., Wang, B. & Lv, S. (2014). Using multi-class AdaBoost tree for prediction frequency of auto insurance. Journal of Applied Finance and Banking 4, 45–53.
- Mayr, A., Binder, H., Gefeller, O. & Schmid, M. (2014a). The evolution of boosting algorithms – from machine learning to statistical modelling. Methods of Information in Medicine 53, 419–427.
- Mayr, A., Binder, H., Gefeller, O. & Schmid, M. (2014b). Extending statistical boosting – an overview of recent methodological developments. Methods of Information in Medicine 53, 428–435.
- Ohlsson, E. & Johansson, B. (2010). Non-life insurance pricing with generalized linear models. Berlin, Heidelberg: Springer-Verlag.
- Pesantez-Narvaez, J., Guillen, M. & Alcaniz, M. (2019). Predicting motor insurance claims using telematics data – XGBoost versus logistic regression. Risks 7, 1–16.
- Therneau, T. M. & Atkinson, B. (2018). rpart: recursive partitioning and regression trees. https://cran.r-project.org/package=rpart.
- Tutz, G. & Binder, H. (2006). Generalized additive modelling with implicit variable selection by likelihood based boosting. Biometrics 51, 961–971.
- Tutz, G. & Binder, H. (2007). Boosting ridge regression. Computational Statistics and Data Analysis 51, 6044–6059.
- Wüthrich, M. V. & Buser, C. (2019). Data analytics for non-life insurance pricing. Lecture notes available at SSRN. https://doi.org/10.2139/ssrn.2870308
- Yang, Y., Qian, W. & Zou, H. (2018). Insurance premium prediction via gradient tree-boosted tweedie compound Poisson models. Journal of Business & Economic Statistics 36, 456–470.