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A NEW INFERENCE STRATEGY FOR GENERAL POPULATION MORTALITY TABLES

Part of: Survival analysis and censored data Applications

Published online by Cambridge University Press:  17 April 2020

Alexandre Boumezoued ,
Marc Hoffmann  and
Paulien Jeunesse
Alexandre Boumezoued*
Affiliation:
Milliman R&D, 14 Avenue de la Grande Armée, 75017Paris, France, E-mail: [email protected]
Marc Hoffmann
Affiliation:
CEREMADE, CNRS-UMR 7534, Universite Paris Dauphine, Place du maréchal de Lattre de Tassigny, 75016Paris, France, E-mail: [email protected]
Paulien Jeunesse
Affiliation:
CEREMADE, CNRS-UMR 7534, Universite Paris Dauphine, Place du maréchal de Lattre de Tassigny, 75016Paris, France, E-mail: [email protected]
*
E-mail: [email protected]
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Abstract

We propose a new inference strategy for general population mortality tables based on annual population and death estimates, completed by monthly birth counts. We rely on a deterministic population dynamics model and establish formulas that link the death rates to be estimated with the observables at hand. The inference algorithm takes the form of a recursive and implicit scheme for computing death rate estimates. This paper demonstrates both theoretically and numerically the efficiency of using additional monthly birth counts for appropriately computing annual mortality tables. As a main result, the improved mortality estimators show better features, including the fact that previous anomalies in the form of isolated cohort effects disappear, which confirms from a mathematical perspective the previous contributions by Richards, Cairns et al., and Boumezoued.

Keywords

Mortality tablesgeneral populationdeath rate inferencepopulation dynamicscohort effectC020

MSC classification

Primary: 92D25: Population dynamics (general)
Secondary: 62P05: Applications to actuarial sciences and financial mathematics 62N02: Estimation
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 50 , Issue 2 , May 2020 , pp. 325 - 356
Copyright
© Astin Bulletin 2020

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