Artículos de revistas
Bayesian analysis of spatial data using different variance and neighbourhood structures
Fecha
2016-02-11Registro en:
Journal Of Statistical Computation And Simulation. Abingdon: Taylor & Francis Ltd, v. 86, n. 3, p. 535-552, 2016.
0094-9655
10.1080/00949655.2015.1022549
WOS:000364339300008
WOS000364339300008.pdf
7939791175456786
0000-0001-7385-6705
Autor
Universidade Estadual Paulista (Unesp)
Institución
Resumen
In disease mapping, the overall goal is to study the incidence or mortality risk caused by a specific disease in a number of geographical regions. It is common to assume that the response variable follows a Poisson distribution, whose average rate can be explained by a group of covariates and a random effect. For this random effect, it is considered conditional autoregressive (CAR) models, which carry information about the neighbourhood relationship between the regions. The focus of this paper was to explore and compare some CAR models proposed in the literature. An application with epidemiological data was conducted to model the risk of death due to Crohn's Disease and Ulcerative Colitis in the State of SAo Paulo - Brazil. Finally, a simulation study was done to strengthen the results and assess the performance of the models in the presence of various levels of spatial dependence.