Risks Special Issue on “Granular Models and Machine Learning Models”
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References listed on IDEAS
- Jacky H. L. Poon, 2019. "Penalising Unexplainability in Neural Networks for Predicting Payments per Claim Incurred," Risks, MDPI, vol. 7(3), pages 1-11, September.
- Francis Duval & Mathieu Pigeon, 2019. "Individual Loss Reserving Using a Gradient Boosting-Based Approach," Risks, MDPI, vol. 7(3), pages 1-18, July.
- Massimo De Felice & Franco Moriconi, 2019. "Claim Watching and Individual Claims Reserving Using Classification and Regression Trees," Risks, MDPI, vol. 7(4), pages 1-36, October.
- Kevin Kuo, 2018. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Papers 1804.09253, arXiv.org, revised Sep 2019.
- Kevin Kuo, 2019. "DeepTriangle: A Deep Learning Approach to Loss Reserving," Risks, MDPI, vol. 7(3), pages 1-12, September.
- Greg Taylor, 2019. "Loss Reserving Models: Granular and Machine Learning Forms," Risks, MDPI, vol. 7(3), pages 1-18, July.
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