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Bayesian analysis of mortality data

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Congdon argued that the use of parametric modelling of mortality data is necessary in many practical demographical problems. In this paper, we focus on a form of model introduced by Heligman and Pollard in 1980, and we adopt a Bayesian analysis, using Markov chain Monte Carlo simulation, to produce the posterior summaries required. This opens the way to richer, more flexible inference summaries and avoids the numerical problems that are encountered with classical methods. Particular methodologies to cope with incomplete life-tables and a derivation of joint lifetimes, median times to death and related quantities of interest are also presented.
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Keywords: Age; Bayesian inference; Incomplete life-table; Markov chain Monte Carlo methods; Mortality data

Document Type: Research Article

Affiliations: 1: Athens University of Economics and Business, Greece 2: Queen Mary and Westfield College, London, UK 3: University of Glasgow, UK

Publication date: 01 February 2001

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