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Abstract Details
Activity Number:
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658
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #301108 |
Title:
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Estimating Demographic Parameters and Their Uncertainty from Fragmentary Data, with Applications
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Author(s):
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Mark Christopher Wheldon*+ and Adrian Raftery and Samuel J. Clark and Patrick Gerland
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Companies:
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University of Washington and University of Washington and University of Washington and United Nations
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Address:
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Center for Statistics and the Social Sciences, Seattle, WA, 98195-4320,
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Keywords:
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Demography ;
Fertility and mortality rates ;
Migration ;
Cohort component model ;
Bayesian hierarchical Model ;
Markov chain Monte Carlo
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Abstract:
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Official statistical agencies regularly compile and publish country-level estimates of population size, fertility and mortality rates and net migration. In some cases, these are based on several data sources, each of varying quality and coverage. These numerous and noisy data sources are commonly combined without formal assessment and coherent synthesis of uncertainty due to measurement error. We propose a new method for simultaneously estimating these key demographic parameters that incorporates measurement error. Inference is based on joint posterior probability distributions which yield fully probabilistic interval estimates. It is designed for the kind of data commonly collected in modern demographic surveys and censuses. Population dynamics over the period of reconstruction are modeled by embedding formal demographic accounting relationships in a Bayesian hierarchical model. Informative priors are specified for vital rates, migration rates, population counts at baseline, and the accuracies of their respective measurements. The method is demonstrated using real data from censuses and surveys.
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