Listar por autor UPNA "Orozco Acosta, Erick"
Mostrando ítems 1-3 de 3
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Bayesian modeling approach in Big Data contexts: an application in spatial epidemiology
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 ... -
Big problems in spatio-temporal disease mapping: methods and software
Background and objective: Fitting spatio-temporal models for areal data is crucial in many fields such as cancer epidemiology. However, when data sets are very large, many issues arise. The main objective of this paper is ... -
Scalable Bayesian modeling for smoothing disease mapping risks in large spatial data sets using INLA
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 ...