Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Wuthrich, Mario V.
2013.
Non-Life Insurance: Mathematics & Statistics.
SSRN Electronic Journal,
Wuthrich, Mario V.
and
Buser, Christoph
2017.
Data Analytics for Non-Life Insurance Pricing.
SSRN Electronic Journal ,
Xu, Yanbin
Pan, Guangming
and
Zhu, Wenjun
2019.
Optimal Risk Pooling for Area-Yield Insurance Design: A Machine-Learning Approach.
SSRN Electronic Journal ,
Lim, Hong Beng
and
Shyamalkumar, Nariankadu
2021.
Incorporating Industry Stylized Facts into Mortality Tables: Transfer Learning with Monotonicity Constraints.
SSRN Electronic Journal ,
Richman, Ronald
2021.
AI in actuarial science – a review of recent advances – part 2.
Annals of Actuarial Science,
Vol. 15,
Issue. 2,
p.
230.
Scognamiglio, Salvatore
2022.
CALIBRATING THE LEE-CARTER AND THE POISSON LEE-CARTER MODELS VIA NEURAL NETWORKS.
ASTIN Bulletin,
Vol. 52,
Issue. 2,
p.
519.
Nigri, Andrea
Levantesi, Susanna
and
Aburto, Josè Manuel
2022.
Leveraging deep neural networks to estimate age-specific mortality from life expectancy at birth.
Demographic Research,
Vol. 47,
Issue. ,
p.
199.
Gao, Guangyuan
Wang, He
and
Wüthrich, Mario V.
2022.
Boosting Poisson regression models with telematics car driving data.
Machine Learning,
Vol. 111,
Issue. 1,
p.
243.
Embrechts, Paul
and
Wüthrich, Mario V.
2022.
Recent Challenges in Actuarial Science.
Annual Review of Statistics and Its Application,
Vol. 9,
Issue. 1,
p.
119.
Manathunga, Vajira
and
Zhu, Danlei
2022.
Unearned premium risk and machine learning techniques.
Frontiers in Applied Mathematics and Statistics,
Vol. 8,
Issue. ,
Richman, Ronald
and
Wuthrich, Mario V.
2023.
Conditional Expectation Network for SHAP.
SSRN Electronic Journal,
Perla, Francesca
and
Scognamiglio, Salvatore
2023.
Locally-coherent multi-population mortality modelling via neural networks.
Decisions in Economics and Finance,
Vol. 46,
Issue. 1,
p.
157.
Zappa, Diego
Clemente, Gian Paolo
Della Corte, Francesco
and
Savelli, Nino
2023.
Editorial on the Special Issue on Insurance: complexity, risks and its connection with social sciences.
Quality & Quantity,
Vol. 57,
Issue. S2,
p.
125.
Wüthrich, Mario V.
and
Merz, Michael
2023.
Statistical Foundations of Actuarial Learning and its Applications.
p.
267.
Zhu, Wenjun
2023.
A Deep Factor Model for Crop Yield Forecasting and Insurance Ratemaking.
North American Actuarial Journal,
p.
1.
Delong, Łukasz
and
Kozak, Anna
2023.
The use of autoencoders for training neural networks with mixed categorical and numerical features.
ASTIN Bulletin,
Vol. 53,
Issue. 2,
p.
213.
Tzougas, George
and
Kutzkov, Konstantin
2023.
Enhancing Logistic Regression Using Neural Networks for Classification in Actuarial Learning.
Algorithms,
Vol. 16,
Issue. 2,
p.
99.
Richman, Ronald
and
Wüthrich, Mario V.
2024.
High-cardinality categorical covariates in network regressions.
Japanese Journal of Statistics and Data Science,
Richman, Ronald
and
Wüthrich, Mario V.
2024.
Smoothness and monotonicity constraints for neural networks using ICEnet.
Annals of Actuarial Science,
p.
1.
Avanzi, Benjamin
Taylor, Greg
Wang, Melantha
and
Wong, Bernard
2024.
Machine Learning with High-Cardinality Categorical Features in Actuarial Applications.
ASTIN Bulletin,
Vol. 54,
Issue. 2,
p.
213.