| Time-series forecasting of mortality rates using deep learning F Perla, R Richman, S Scognamiglio, MV Wüthrich Scandinavian Actuarial Journal 2021 (7), 572-598, 2021 | 94 | 2021 |
| A deep learning integrated Lee–Carter model A Nigri, S Levantesi, M Marino, S Scognamiglio, F Perla Risks 7 (1), 33, 2019 | 86 | 2019 |
| Calibrating the lee-carter and the poisson lee-carter models via neural networks S Scognamiglio ASTIN Bulletin: The Journal of the IAA 52 (2), 519-561, 2022 | 20 | 2022 |
| An investigation of machine learning approaches in the solvency ii valuation framework G Castellani, U Fiore, Z Marino, L Passalacqua, F Perla, S Scognamiglio, ... Available at SSRN 3303296, 2018 | 16 | 2018 |
| A deep learning integrated Lee-Carter model. Risks, 7 (1), 33 A Nigri, S Levantesi, M Marino, S Scognamiglio, F Perla | 9 | 2019 |
| l1-Regularization in Portfolio Selection with Machine Learning S Corsaro, V De Simone, Z Marino, S Scognamiglio Mathematics 10 (4), 540, 2022 | 8 | 2022 |
| Locally-coherent multi-population mortality modelling via neural networks F Perla, S Scognamiglio Decisions in Economics and Finance 46 (1), 157-176, 2023 | 7 | 2023 |
| A new dynamic and perspective parsimonious AHP model for improving industrial frameworks G Fattoruso, S Scognamiglio, A Violi Mathematics 10 (17), 3138, 2022 | 7 | 2022 |
| Machine learning techniques in nested stochastic simulations for life insurance G Castellani, U Fiore, Z Marino, L Passalacqua, F Perla, S Scognamiglio, ... Applied Stochastic Models in Business and Industry 37 (2), 159-181, 2021 | 7 | 2021 |
| Backtesting stochastic mortality models by prediction interval-based metrics S Scognamiglio, M Marino Quality & Quantity 57 (4), 3825-3847, 2023 | 5 | 2023 |
| Deep learning forecasting for supporting terminal operators in port business development M Ferretti, U Fiore, F Perla, M Risitano, S Scognamiglio Future Internet 14 (8), 221, 2022 | 5 | 2022 |
| Tuning a deep learning network for solvency ii: Preliminary results U Fiore, Z Marino, L Passalacqua, F Perla, S Scognamiglio, P Zanetti Mathematical and Statistical Methods for Actuarial Sciences and Finance: MAF …, 2018 | 5 | 2018 |
| Longevity risk analysis: applications to the Italian regional data S Scognamiglio Quantitative Finance and Economics 6 (1), 138-157, 2022 | 4 | 2022 |
| Robust classification via support vector machines AV Asimit, I Kyriakou, S Santoni, S Scognamiglio, R Zhu Risks 10 (8), 154, 2022 | 3 | 2022 |
| A multi-population locally-coherent mortality model S Scognamiglio Methods and Applications in Fluorescence, 423-428, 2022 | 3 | 2022 |
| Accurate and explainable mortality forecasting with the LocalGLMnet F Perla, R Richman, S Scognamiglio, MV Wüthrich Scandinavian Actuarial Journal, 1-23, 2024 | 2 | 2024 |
| Multiple Yield Curve Modeling and Forecasting using Deep Learning R Richman, S Scognamiglio arXiv preprint arXiv:2401.16985, 2024 | 1 | 2024 |
| Machine learning in nested simulations under actuarial uncertainty G Castellani, U Fiore, Z Marino, L Passalacqua, F Perla, S Scognamiglio, ... Mathematical and Statistical Methods for Actuarial Sciences and Finance …, 2021 | 1 | 2021 |
| Effectiveness of investments in prevention of geological disasters U Fiore, Z Marino, F Perla, M Pietroluongo, S Scognamiglio, P Zanetti Dynamics of Disasters: Impact, Risk, Resilience, and Solutions, 101-108, 2020 | 1 | 2020 |
| Disaggregating Death Rates of Age-Groups Using Deep Learning Algorithms A Nigri, S Levantesi, S Scognamiglio Journal of Official Statistics, 0282423X241240739, 2024 | | 2024 |
