  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.
