Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas

Multivariate models for spatial count data are currently receiving attention in disease mapping to model two or more diseases jointly. They have been thoroughly studied from a theoretical point of view, but their use in practice is still limited because they are computationally expensive and, in gen...

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Detalhes bibliográficos
Autores: Vicente Fuenzalida, Gonzalo, Goicoa Mangado, Tomás, Ugarte Martínez, María Dolores
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2020
País:España
Recursos:Universidad Pública de Navarra
Repositório:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/37270
Acesso em linha:https://hdl.handle.net/2454/37270
Access Level:Acceso aberto
Palavra-chave:Crimes against women
Dowry deaths
Rapes
Gibbs sampling
Hierarchical Bayesian models
INLA
M-models
WinBUGS
Descrição
Resumo:Multivariate models for spatial count data are currently receiving attention in disease mapping to model two or more diseases jointly. They have been thoroughly studied from a theoretical point of view, but their use in practice is still limited because they are computationally expensive and, in general, they are not implemented in standard software to be used routinely. Here, a new multivariate proposal, based on the recently derived M models for spatial data, is developed for spatio-temporal areal data. The model takes account of the correlation between the spatial and temporal patterns of the phenomena being studied, and it also includes spatio-temporal interactions. Though multivariate models have been traditionally fitted using Markov chain Monte Carlo techniques, here we propose to adopt integrated nested Laplace approximations to speed up computations as results obtained using both fitting techniques were nearly identical. The techniques are used to analyse two forms of crimes against women in India. In particular, we focus on the joint analysis of rapes and dowry deaths in Uttar Pradesh, the most populated Indian state, during the years 2001-2014.