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Bayesian age-period-cohort modeling and prediction - BAMP


Schmid, V J; Held, L (2007). Bayesian age-period-cohort modeling and prediction - BAMP. Journal of Statistical Software, 21(8):1-15.

Abstract

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted.

Abstract

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted.

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Citations

24 citations in Web of Science®
25 citations in Scopus®
63 citations in Microsoft Academic
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209 downloads since deposited on 16 Apr 2009
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:October 2007
Deposited On:16 Apr 2009 08:41
Last Modified:04 Jun 2018 04:37
Publisher:Foundation for Open Access Statistics
ISSN:1548-7660
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Official URL:http://www.jstatsoft.org/v21/i08

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