Mortality modeling and regression with matrix distributions
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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.
|Insurance: Mathematics and Economics
|Udgivet - 2022
The authors would like to thank Johannes Thuswaldner for conscientiously implementing some tests for the fitting procedures at an early stage of the project, and Peter Hieber for interesting discussions on the topic. HA and MaB would like to acknowledge financial support from the Swiss National Science Foundation Project 200021_191984 . JY would like to acknowledge financial support from the Swiss National Science Foundation Project IZHRZ0_180549 .
© 2022 The Author(s)