Scalable Bayesian modeling for smoothing disease mapping risks in large spatial data sets using INLA
Fecha
2021Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
Impacto
|
10.1016/j.spasta.2021.100496
Resumen
Several methods have been proposed in the spatial statistics literature to analyse big data sets in continuous domains. However, new methods for analysing high-dimensional areal data are still scarce. Here, we propose a scalable Bayesian modelling approach for smoothing mortality (or incidence) risks in high-dimensional data, that is, when the number of small areas is very large. The method is im ...
[++]
Several methods have been proposed in the spatial statistics literature to analyse big data sets in continuous domains. However, new methods for analysing high-dimensional areal data are still scarce. Here, we propose a scalable Bayesian modelling approach for smoothing mortality (or incidence) risks in high-dimensional data, that is, when the number of small areas is very large. The method is implemented in the R add-on package bigDM and it is based on the idea of “divide and conquer“. Although this proposal could possibly be implemented using any Bayesian fitting technique, we use INLA here (integrated nested Laplace approximations) as it is now a well-known technique, computationally efficient, and easy for practitioners to handle. We analyse the proposal’s empirical performance in a comprehensive simulation study that considers two model-free settings. Finally, the methodology is applied to analyse male colorectal cancer mortality in Spanish municipalities showing its benefits with regard to the standard approach in terms of goodness of fit and computational time. [--]
Materias
High-dimensional data,
Hierarchical models,
Mixture models,
Spatial epidemiology
Editor
Elsevier
Publicado en
Spatial Statistics 41 (2021) 100496
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute for Advanced Materials and Mathematics - INAMAT2
Versión del editor
Entidades Financiadoras
This research has been supported by the Spanish Ministry of Science and Innovation (project
MTM 2017-82553-R (AEI/FEDER, UE)). It has also been partially funded by la Caixa Foundation, Spain
(ID 1000010434), Caja Navarra Foundation, Spain, and UNED Pamplona, Spain, under agreement
LCF/PR/PR15/51100007 (project REF P/13/20).