Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106727
Title: An application of MCMC simulation in mortality projection for populations with limited data
Authors: Li, Jackie
Keywords: DRNTU::Engineering::Computer science and engineering::Data
Issue Date: 2014
Source: Li, J. (2014). An application of MCMC simulation in mortality projection for populations with limited data. Demographic research, 30, 1-48.
Series/Report no.: Demographic research
Abstract: In this paper, we investigate the use of Bayesian modeling and Markov chain Monte Carlo (MCMC) simulation, via the software WinBUGS, to project future mortality for populations with limited data. In particular, we adapt some extensions of the Lee-Carter method under the Bayesian framework to allow for situations in which mortality data are scarce. Our approach would be useful for certain developing nations that have not been regularly collecting death counts and population statistics. Inferences of the model estimates and forecasts can readily be drawn from the simulated samples. Information on another population resembling the population under study can be exploited and incorporated into the prior distributions in order to facilitate the construction of probability intervals. The two sets of data can also be modeled in a joint manner. We demonstrate an application of this approach to some data from China and Taiwan.
URI: https://hdl.handle.net/10356/106727
http://hdl.handle.net/10220/25099
ISSN: 1435-9871
DOI: 10.4054/DemRes.2014.30.1
Rights: © 2014 Jackie Li. This open-access work is published under the terms of the Creative Commons Attribution NonCommercial License 2.0 Germany, which permits use, reproduction & distribution in any medium for non-commercial purposes, provided the original author(s) and source are given credit. See http://creativecommons.org/licenses/by-nc/2.0/de/
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:NBS Journal Articles

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