Bayesian modeling approach in Big Data contexts: an application in spatial epidemiology

Date

2020

Director

Publisher

IEEE
Acceso abierto / Sarbide irekia
Contribución a congreso / Biltzarrerako ekarpena
Versión aceptada / Onetsi den bertsioa

Project identifier

  • AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-82553-R/ES/ recolecta
Impacto
OpenAlexGoogle Scholar
cited by count

Abstract

In this work we propose a novel scalable Bayesian modeling approach to smooth mortality risks borrowing information from neighbouring regions in high-dimensional spatial disease mapping contexts. The method is based on the well-known divide and conquer approach, so that the spatial domain is divided into D subregions where local spatial models can be fitted simultaneously. Model fitting and inference has been carried out using the integrated nested Laplace approximation (INLA) technique. Male colorectal cancer mortality data in the municipalities of continental Spain have been analyzed using the new model proposals. Results show that the new modeling approach is very competitive in terms of model fitting criteria when compared with a global spatial model, and it is computationally much more efficient.

Description

Keywords

Disease mapping, High-dimensional data, INLA, Parallel computing

Department

Estatistika, Informatika eta Matematika / Institute for Advanced Materials and Mathematics - INAMAT2 / Estadística, Informática y Matemáticas

Faculty/School

Degree

Doctorate program

item.page.cita

E. Orozco-Acosta, A. Adin and M. D. Ugarte, 'Bayesian Modeling Approach in Big Data Contexts: an Application in Spatial Epidemiology', 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020, pp. 749-750, doi: 10.1109/DSAA49011.2020.00097.

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