| A class of mixture of experts models for general insurance: Application to correlated claim frequencies TC Fung, AL Badescu, XS Lin ASTIN Bulletin: The Journal of the IAA 49 (3), 647-688, 2019 | 43 | 2019 |
| A class of mixture of experts models for general insurance: Theoretical developments TC Fung, AL Badescu, XS Lin Insurance: Mathematics and Economics 89, 111-127, 2019 | 31 | 2019 |
| Fitting censored and truncated regression data using the mixture of experts models TC Fung, AL Badescu, XS Lin North American Actuarial Journal 26 (4), 496-520, 2022 | 22 | 2022 |
| Mixture composite regression models with multi-type feature selection TC Fung, G Tzougas, MV Wüthrich North American Actuarial Journal 27 (2), 396-428, 2023 | 18 | 2023 |
| A new class of severity regression models with an application to IBNR prediction TC Fung, AL Badescu, XS Lin North American Actuarial Journal 25 (2), 206-231, 2021 | 18 | 2021 |
| LRMoE: an R package for flexible actuarial loss modelling using mixture of experts regression model SC Tseung, A Badescu, TC Fung, XS Lin Available at SSRN 3740215, 2020 | 10 | 2020 |
| LRMoE. jl: a software package for insurance loss modelling using mixture of experts regression model SC Tseung, AL Badescu, TC Fung, XS Lin Annals of Actuarial Science 15 (2), 419-440, 2021 | 8 | 2021 |
| Multivariate Cox hidden Markov models with an application to operational risk TC Fung, AL Badescu, XS Lin Scandinavian Actuarial Journal 2019 (8), 686-710, 2019 | 8 | 2019 |
| Multivariate claim count regression model with varying dispersion and dependence parameters H Jeong, G Tzougas, TC Fung Journal of the Royal Statistical Society Series A: Statistics in Society 186 …, 2023 | 6 | 2023 |
| Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models TC Fung Insurance: Mathematics and Economics 107, 180-198, 2022 | 6 | 2022 |
| A posteriori risk classification and ratemaking with random effects in the mixture-of-experts model SC Tseung, IW Chan, TC Fung, AL Badescu, XS Lin arXiv preprint arXiv:2209.15212, 2022 | 2 | 2022 |
| Mixture of experts models for multilevel data: Modelling framework and approximation theory TC Fung, SC Tseung arXiv preprint arXiv:2209.15207, 2022 | 2 | 2022 |
| Robust estimation and model diagnostic of insurance loss data: a weighted likelihood approach TC Fung arXiv preprint arXiv:2204.10459, 2022 | 1 | 2022 |
| A Revisit of the Optimal Excess-of-Loss Contract E Aboagye, V Asimit, TC Fung, L Peng, Q Wang arXiv preprint arXiv:2405.00188, 2024 | | 2024 |
| Robust estimation and diagnostic of generalized linear model for insurance losses: a weighted likelihood approach TC Fung Metrika, 1-34, 2024 | | 2024 |
| Soft splicing model: bridging the gap between composite model and finite mixture model TC Fung, H Jeong, G Tzougas Scandinavian Actuarial Journal 2024 (2), 168-197, 2024 | | 2024 |
| Investigating the effect of climate-related hazards on claim frequency prediction in motor insurance TC Fung, H Jeong, G Tzougas Available at SSRN 4638074, 2023 | | 2023 |
| Diagnostic tests before modeling longitudinal actuarial data Y Li, TC Fung, L Peng, L Qian Insurance: Mathematics and Economics 113, 310-325, 2023 | | 2023 |
| Improving risk classification and ratemaking using mixture‐of‐experts models with random effects SC Tseung, IW Chan, TC Fung, AL Badescu, XS Lin Journal of Risk and Insurance 90 (3), 789-820, 2023 | | 2023 |
| A Class of Mixture of Experts Models for General Insurance Ratemaking and Reserving TCS Fung University of Toronto (Canada), 2020 | | 2020 |
