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Scandinavian Actuarial Journal Volume 2023, 2023 - Issue 1
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Research Article

LocalGLMnet: interpretable deep learning for tabular data

Ronald Richmana Old Mutual Insure and University of the Witwatersrand, Johannesburg, South Africa
&
Mario V. Wüthrichb Department of Mathematics, RiskLab, ETH Zurich, Zurich, SwitzerlandCorrespondence[email protected]
Pages 71-95 | Received 23 Jul 2021, Accepted 20 May 2022, Published online: 08 Jun 2022

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Francesca Perla, Ronald Richman, Salvatore Scognamiglio & Mario V. Wüthrich. Accurate and explainable mortality forecasting with the LocalGLMnet. Scandinavian Actuarial Journal 0:0, pages 1-23.
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Alex Jose, Angus S. Macdonald, George Tzougas & George Streftaris. (2024) Interpretable zero-inflated neural network models for predicting admission counts. Annals of Actuarial Science, pages 1-31.
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Mario V. Wuthrich. (2024) Experience Rating in Insurance Pricing. SSRN Electronic Journal.
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Yevhen Havrylenko & Julia Heger. (2023) Detection of interacting variables for generalized linear models via neural networks. European Actuarial Journal.
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Tommy Liu & Amanda S. Barnard. (2023) The emergent role of explainable artificial intelligence in the materials sciences. Cell Reports Physical Science 4:10, pages 101630.
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Gian Paolo Clemente, Francesco Della Corte, Nino Savelli & Diego Zappa. (2023) Special Issue “Data Science in Insurance”. Risks 11:5, pages 80.
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Alex Jose, Angus S. Macdonald, George Tzougas & George Streftaris. (2022) A Combined Neural Network Approach for the Prediction of Admission Rates Related to Respiratory Diseases. Risks 10:11, pages 217.
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Mario V. Wuthrich & Christoph Buser. (2017) Data Analytics for Non-Life Insurance Pricing. SSRN Electronic Journal.
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Mario V. Wuthrich. (2013) Non-Life Insurance: Mathematics & Statistics. SSRN Electronic Journal.
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