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Correlated GMRF priors for multivariate age-period-cohort models


Riebler, A; Held, L; Rue, H (2010). Correlated GMRF priors for multivariate age-period-cohort models. In: 25th International Workshop on Statistical Modelling, Glasgow, 5 July 2010 - 9 July 2010, 1-6.

Abstract

Multivariate age-period-cohort models have recently been proposed for the analysis of heterogeneous time trends. For a fully Bayesian analysis, Gaussian Markov random field (GMRF) priors are typically used. However, standard GMRF priors do not account for a potential dependence between outcomes. We present an extended approach based on correlated smoothing priors and corre-lated overdispersion parameters. Algorithmic routines are based on either Markov chain Monte Carlo or integrated nested Laplace approximations. Results are discussed for data on female mortality in Denmark and Norway and compared by means of DIC, proper scoring rules and the marginal likelihood.

Abstract

Multivariate age-period-cohort models have recently been proposed for the analysis of heterogeneous time trends. For a fully Bayesian analysis, Gaussian Markov random field (GMRF) priors are typically used. However, standard GMRF priors do not account for a potential dependence between outcomes. We present an extended approach based on correlated smoothing priors and corre-lated overdispersion parameters. Algorithmic routines are based on either Markov chain Monte Carlo or integrated nested Laplace approximations. Results are discussed for data on female mortality in Denmark and Norway and compared by means of DIC, proper scoring rules and the marginal likelihood.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Event End Date:9 July 2010
Deposited On:06 Jan 2011 11:45
Last Modified:07 Dec 2017 04:22

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