City Research Online

Abstract

Cause-of-death mortality modeling and forecasting is an important topic in demography and actuarial science, as it can provide valuable insights into the risks and factors determining future mortality rates. In this paper, we propose a novel predictive approach for cause-of-death mortality forecasting, based on an adaptive penalized tensor decomposition (ADAPT). The new method jointly models the three dimensions (cause, age, and year) of the data, and uses adaptively weighted penalty matrices to overcome the computational burden of having to select a large number of tuning parameters when multiple factors are involved. ADAPT can be coupled with a variety of methods (e.g., linear extrapolation, and smoothing) for extrapolating the estimated year factors and hence for mortality forecasting. Based on an application to United States (US) male cause-of-death mortality data, we demonstrate that tensor decomposition methods such as ADAPT can offer strong out-of-sample predictive performance compared to several existing models, especially when it comes to mid- and long-term forecasting.

Publication Type:	Article
Additional Information:	© 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords:	Adaptive Weights, Causes of death, Generalized Lasso Penalty, Model Selection, Tensor Decomposition
Subjects:	G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography H Social Sciences > HN Social history and conditions. Social problems. Social reform
Departments:	Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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