Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Chen, An
Kanagawa, Motonobu
and
Zhang, Fangyuan
2021.
Intergenerational Risk Sharing in a Collective Defined-Contribution Pension System: A Simulation Study with Bayesian Optimization.
SSRN Electronic Journal ,
Nigri, Andrea
Levantesi, Susanna
and
Aburto, Jose Manuel
2022.
Leveraging deep neural networks to estimate age-specific mortality from life expectancy at birth.
Demographic Research,
Vol. 47,
Issue. ,
p.
199.
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.
Scognamiglio, Salvatore
and
Marino, Mario
2023.
Backtesting stochastic mortality models by prediction interval-based metrics.
Quality & Quantity,
Vol. 57,
Issue. 4,
p.
3825.
Santolino, Miguel
2023.
Should Selection of the Optimum Stochastic Mortality Model Be Based on the Original or the Logarithmic Scale of the Mortality Rate?.
Risks,
Vol. 11,
Issue. 10,
p.
170.
Chen, An
Kanagawa, Motonobu
and
Zhang, Fangyuan
2023.
Intergenerational risk sharing in a defined contribution pension system: analysis with Bayesian optimization.
ASTIN Bulletin,
Vol. 53,
Issue. 3,
p.
515.
Zuo, Wenyun
Damle, Anil
and
Tuljapurkar, Shripad
2024.
Sensitivity and uncertainty in the Lee–Carter mortality model.
International Journal of Forecasting,
Dimai, Matteo
2024.
Clustering of mortality paths with the Hellinger distance and visualization through the DISTATIS technique.
SSRN Electronic Journal,
Scognamiglio, Salvatore
2024.
Multi-population mortality modelling and forecasting with divergence bounds.
Annals of Operations Research,
Dimai, Matteo
and
Brabec, Marek
2024.
Mathematical and Statistical Methods for Actuarial Sciences and Finance.
p.
149.
Nigri, Andrea
Levantesi, Susanna
and
Scognamiglio, Salvatore
2024.
Disaggregating Death Rates of Age-Groups Using Deep Learning Algorithms.
Journal of Official Statistics,
Vol. 40,
Issue. 2,
p.
262.
Dimai, Matteo
and
Brabec, Marek
2024.
A Bayesian Model for Age at Death with Cohort Effects.
SSRN Electronic Journal,
Dimai, Matteo
2024.
Multi-population mortality modeling with economic, environmental and lifestyle variables.
Quality & Quantity,
Wang, Jun
Wen, Lihong
Xiao, Lu
and
Wang, Chaojie
2024.
Time-series forecasting of mortality rates using transformer.
Scandinavian Actuarial Journal,
Vol. 2024,
Issue. 2,
p.
109.
Richman, Ronald
and
Scognamiglio, Salvatore
2024.
Multiple yield curve modeling and forecasting using deep learning.
ASTIN Bulletin,
Vol. 54,
Issue. 3,
p.
463.
Perla, Francesca
Richman, Ronald
Scognamiglio, Salvatore
and
Wüthrich, Mario V.
2024.
Accurate and explainable mortality forecasting with the LocalGLMnet.
Scandinavian Actuarial Journal,
Vol. 2024,
Issue. 7,
p.
739.
Dimai, Matteo
and
Brabec, Marek
2024.
A Bayesian model for age at death with cohort effects.
Demographic Research,
Vol. 51,
Issue. ,
p.
1017.
Euthum, Maximilian
Scherer, Matthias
and
Ungolo, Francesco
2024.
A neural network approach for the mortality analysis of multiple populations: a case study on data of the Italian population.
European Actuarial Journal,
Vol. 14,
Issue. 2,
p.
495.
Shen, Yuewen
Yang, Xinhao
Liu, Hao
and
Li, Ze
2024.
Advancing mortality rate prediction in European population clusters: integrating deep learning and multiscale analysis.
Scientific Reports,
Vol. 14,
Issue. 1,