Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China

Geostatistics data in regions always have highly spatial heterogeneous, yet the regional features of the data itself cannot be ignored. In this paper, a novel latent Bayesian spatial model is proposed, which incorporates the spatial dependence of different subjects and the spatial random effects to...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-10, Vol.11 (19), p.4193
Main Authors: Xia, Zongyuan, Tang, Bo, Qin, Long, Zhang, Huiguo, Hu, Xijian
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Bayesian spatial model
Book publishing
Differential equations
Econometrics
Geology
Geostatistics
INLA-SPDE
Model accuracy
nonparametric
Nonparametric statistics
Normal distribution
Parameter estimation
Partial differential equations
Software
spatial denpendence
Statistical methods
Tuberculosis
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Summary:Geostatistics data in regions always have highly spatial heterogeneous, yet the regional features of the data itself cannot be ignored. In this paper, a novel latent Bayesian spatial model is proposed, which incorporates the spatial dependence of different subjects and the spatial random effects to further analysis the influence of spatial effect. The model is verified to be compatible with the integrated nested Laplace approximation (INLA) framework and is fitted using INLA and stochastic partial differential equation (SPDE). The posterior marginal distribution of parameters is estimated with high precision. Additionally, a practical application of the model is presented using tuberculosis (TB) incidence data in China from 2015 to 2019. The results show that the fitting accuracy of our model is higher than the general Bayesian spatial model using INLA-SPDE.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11194193