Mortality modeling and regression with matrix distributions

TL;DR

This paper introduces a flexible matrix distribution-based mortality model that captures aging processes more interpretably and extends to multi-population regression, with efficient estimation methods demonstrated on real data.

Contribution

It develops a novel mortality modeling framework using inhomogeneous phase-type distributions and introduces regression via proportional intensities, enhancing interpretability and flexibility.

Findings

Accurately models mortality curves across lifespan

Provides an EM algorithm for parameter estimation

Demonstrates practical effectiveness on real data

Abstract

In this paper we investigate the flexibility of matrix distributions for the modeling of mortality. Starting from a simple Gompertz law, we show how the introduction of matrix-valued parameters via inhomogeneous phase-type distributions can lead to reasonably accurate and relatively parsimonious models for mortality curves across the entire lifespan. A particular feature of the proposed model framework is that it allows for a more direct interpretation of the implied underlying aging process than some previous approaches. Subsequently, towards applications of the approach for multi-population mortality modeling, we introduce regression via the concept of proportional intensities, which are more flexible than proportional hazard models, and we show that the two classes are asymptotically equivalent. We illustrate how the model parameters can be estimated from data by providing an adapted EM algorithm for which the likelihood increases at each iteration. The practical feasibility and competitiveness of the proposed approach, including the right-censored case, are illustrated by several sets of mortality and survival data.