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Deep quantile and deep composite triplet regression

Fissler, Tobias; Merz, Michael; Wüthrich, Mario V. - In: Insurance 109 (2023), pp. 94-112

Persistent link: https://www.econbiz.de/10014282471

Gamma Mixture Density Networks and their application to modelling insurance claim amounts

Delong, Łukasz; Lindholm, Mathias; Wüthrich, Mario V. - In: Insurance 101 (2021) 2, pp. 240-261

Persistent link: https://www.econbiz.de/10012793926

Time-series forecasting of mortality rates using deep learning

Perla, Francesca; Richman, Ronald; Scognamiglio, Salvatore - In: Scandinavian actuarial journal 2021 (2021) 7, pp. 572-598

Persistent link: https://www.econbiz.de/10012624635

A neural network extension of the Lee-Carter model to multiple populations

Richman, Ronald; Wüthrich, Mario V. - In: Annals of actuarial science 15 (2021) 2, pp. 346-366

Persistent link: https://www.econbiz.de/10012593584

LocalGLMnet : interpretable deep learning for tabular data

Richman, Ronald; Wüthrich, Mario V. - In: Scandinavian actuarial journal 2023 (2023) 1, pp. 71-95

Persistent link: https://www.econbiz.de/10013491052

What can we learn from telematics car driving data : a survey

Gao, Guangyuan; Meng, Shengwang; Wüthrich, Mario V. - In: Insurance 104 (2022), pp. 185-199

Persistent link: https://www.econbiz.de/10013264947

Accurate and explainable mortality forecasting with the LocalGLMnet

Perla, Francesca; Richman, Ronald; Scognamiglio, Salvatore - In: Scandinavian actuarial journal 2024 (2024) 7, pp. 739-761

Persistent link: https://www.econbiz.de/10015052479

Claims frequency modeling using telematics car driving data

Gao, Guangyuan; Meng, Shengwang; Wüthrich, Mario V. - In: Scandinavian actuarial journal 2019 (2019) 2, pp. 143-162

Persistent link: https://www.econbiz.de/10012194942

Neural network embedding of the over-dispersed Poisson reserving model

Gabrielli, Andrea; Richman, Ronald; Wüthrich, Mario V. - In: Scandinavian actuarial journal 2020 (2020) 1, pp. 1-29

Persistent link: https://www.econbiz.de/10012195016

Nagging predictors

Richman, Ronald; Wüthrich, Mario V. - In: Risks : open access journal 8 (2020) 3/83, pp. 1-26

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the...

Persistent link: https://www.econbiz.de/10012293262