Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Ali, Yassmin
Fang, Ming
Sota, Pablo A. Arrutia
Taylor, Stephen
and
Wang, Xun
2019.
Social Security Benefit Valuation, Risk, and Optimal Retirement.
Risks,
Vol. 7,
Issue. 4,
p.
124.
Mashrur, Akib
Luo, Wei
Zaidi, Nayyar A.
and
Robles-Kelly, Antonio
2020.
Machine Learning for Financial Risk Management: A Survey.
IEEE Access,
Vol. 8,
Issue. ,
p.
203203.
Diao, Liqun
Meng, Yechao
and
Weng, Chengguo
2020.
A DSA Algorithm for Mortality Forecasting.
SSRN Electronic Journal ,
Shang, Han Lin
and
Haberman, Steven
2020.
FORECASTING MULTIPLE FUNCTIONAL TIME SERIES IN A GROUP STRUCTURE: AN APPLICATION TO MORTALITY.
ASTIN Bulletin,
Vol. 50,
Issue. 2,
p.
357.
Dhamodharavadhani, S
Rathipriya, R
and
Chatterjee, Jyotir Moy
2020.
COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models.
Frontiers in Public Health,
Vol. 8,
Issue. ,
Hassan, Alla Ahmad
and
Rashid, Tarik A
2021.
A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients.
Kurdistan Journal of Applied Research,
p.
44.
Antonio, Katrien
Dutang, Christophe
and
Tsanakas, Andreas
2021.
Editorial.
Annals of Actuarial Science,
Vol. 15,
Issue. 2,
p.
205.
Bravo, Jorge M.
2021.
Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
Vol. 1525,
Issue. ,
p.
232.
Wuthrich, Mario V.
and
Merz, Michael
2021.
Statistical Foundations of Actuarial Learning and its Applications.
SSRN Electronic Journal ,
Tedesco, Salvatore
Andrulli, Martina
Larsson, Markus Åkerlund
Kelly, Daniel
Alamäki, Antti
Timmons, Suzanne
Barton, John
Condell, Joan
O’Flynn, Brendan
and
Nordström, Anna
2021.
Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults.
International Journal of Environmental Research and Public Health,
Vol. 18,
Issue. 23,
p.
12806.
Nigri, Andrea
Levantesi, Susanna
and
Marino, Mario
2021.
Life expectancy and lifespan disparity forecasting: a long short-term memory approach.
Scandinavian Actuarial Journal,
Vol. 2021,
Issue. 2,
p.
110.
Nieto-Barajas, Luis E.
2022.
Bayesian nonparametric dynamic hazard rates in evolutionary life tables.
Lifetime Data Analysis,
Vol. 28,
Issue. 2,
p.
319.
Scognamiglio, Salvatore
2022.
Longevity risk analysis: applications to the Italian regional data.
Quantitative Finance and Economics,
Vol. 6,
Issue. 1,
p.
138.
Scognamiglio, Salvatore
2022.
Mathematical and Statistical Methods for Actuarial Sciences and Finance.
p.
423.
Miyata, Akihiro
and
Matsuyama, Naoki
2022.
EXTENDING THE LEE–CARTER MODEL WITH VARIATIONAL AUTOENCODER: A FUSION OF NEURAL NETWORK AND BAYESIAN APPROACH.
ASTIN Bulletin,
Vol. 52,
Issue. 3,
p.
789.
Nantwi, Nana Poku Appiagyei
Lotsi, Anani
and
Debrah, Godwin
2022.
Longevity risk—Its financial impact on pensions.
Scientific African,
Vol. 16,
Issue. ,
p.
e01241.
Beyaztas, Ufuk
and
Shang, Hanlin
2022.
Machine-Learning-Based Functional Time Series Forecasting: Application to Age-Specific Mortality Rates.
Forecasting,
Vol. 4,
Issue. 1,
p.
394.
Schnürch, Simon
and
Korn, Ralf
2022.
POINT AND INTERVAL FORECASTS OF DEATH RATES USING NEURAL NETWORKS.
ASTIN Bulletin,
Vol. 52,
Issue. 1,
p.
333.
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.