Mortality modeling and regression with matrix distributions

Hansjörg Albrecher, Martin Bladt, Mogens Bladt and Jorge Yslas

Insurance: Mathematics and Economics, 2022, vol. 107, issue C, 68-87

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.

Keywords: Survival analysis; Regression models; Phase-type distributions; Inhomogeneous phase-type distributions; Inhomogeneous Markov processes (search for similar items in EconPapers)
JEL-codes: C13 G22 J11 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:107:y:2022:i:c:p:68-87

DOI: 10.1016/j.insmatheco.2022.08.001

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