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Joint models for cause-of-death mortality in multiple populations

Abstract
We investigate jointly modelling age–year-specific rates of various causes of death in a multinational setting. We apply multi-output Gaussian processes (MOGPs) a spatial machine learning method to smooth and extrapolate multiple cause-of-death mortality rates across several countries and both genders. To maintain flexibility and scalability we investigate MOGPs with Kronecker-structured kernels and latent factors. In particular we develop a custom multi-level MOGP that leverages the gridded structure of mortality tables to efficiently capture heterogeneity and dependence across different factor inputs. Results are illustrated with datasets from the Human Cause-of-Death Database (HCD). We discuss a case study involving cancer variations in three European nations and a US-based study that considers eight top-level causes and includes comparison to all-cause analysis. Our models provide insights into the commonality of cause-specific mortality trends and demonstrate the opportunities for respective data fusion.
Authors
Nhan Huynh and Mike Ludkovski
Keywords
Rank
0.64
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Machine Learning
Series
Annals of Actuarial Science 2024 vol. 18 issue 1 51-77
Time Added
2024/03/11 03:44
Total Downloads
0
Year Published
2024
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