# Multipopulation mortality modelling and forecasting: The multivariate functional principal component with time weightings approaches

arXiv: https://arxiv.org/abs/2102.09612 · HTML: https://mlsanity.com/paper/2102.09612/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 45 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains](https://mlsanity.com/paper/1509.02029/) (2015)

## Built on by (most cited first, 1 of 1)

- [Random cohort effects and age groups dependency structure for mortality modelling and forecasting: Mixed-effects time-series model approach](https://mlsanity.com/paper/2112.15258/) (2021)

## Abstract (arXiv)

Human mortality patterns and trajectories in closely related populations are likely linked together and share similarities. It is always desirable to model them simultaneously while taking their heterogeneity into account. This paper introduces two new models for joint mortality modelling and forecasting multiple subpopulations in adaptations of the multivariate functional principal component analysis techniques. The first model extends the independent functional data model to a multi-population modelling setting. In the second one, we propose a novel multivariate functional principal component method for coherent modelling. Its design primarily fulfils the idea that when several subpopulation groups have similar socio-economic conditions or common biological characteristics, such close connections are expected to evolve in a non-diverging fashion. We demonstrate the proposed methods by using sex-specific mortality data. Their forecast performances are further compared with several existing models, including the independent functional data model and the Product-Ratio model, through comparisons with mortality data of ten developed countries. Our experiment results show that the first proposed model maintains a comparable forecast ability with the existing methods. In contrast, the second proposed model outperforms the first model as well as the current models in terms of forecast accuracy, in addition to several desirable properties.