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Bayesian analysis of mortality data
Author(s) -
Dellaportas Petros,
Smith Adrian F. M.,
Stavropoulos Photis
Publication year - 2001
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/1467-985x.00202
Subject(s) - markov chain monte carlo , computer science , bayesian probability , inference , focus (optics) , parametric statistics , bayesian inference , posterior probability , econometrics , statistical inference , algorithm , machine learning , artificial intelligence , data mining , mathematics , statistics , physics , optics
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.