Analysis of spatial data with a nested correlation structure

Adegboye, Oyelola A., Leung, Denis H.Y., and Wang, You-Gan (2018) Analysis of spatial data with a nested correlation structure. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67 (2). pp. 329-354.

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Abstract

Summary Spatial statistical analyses are often used to study the link between environmental factors and the incidence of diseases. In modelling spatial data, the existence of spatial correlation between observations must be considered. However, in many situations, the exact form of the spatial correlation is unknown. This paper studies environmental factors that might influence the incidence of malaria in Afghanistan. We assume that spatial correlation may be induced by multiple latent sources. Our method is based on a generalized estimating equation of the marginal mean of disease incidence, as a function of the geographical factors and the spatial correlation. Instead of using one set of generalized estimating equations, we embed a series of generalized estimating equations, each reflecting a particular source of spatial correlation, into a larger system of estimating equations. To estimate the spatial correlation parameters, we set up a supplementary set of estimating equations based on the correlation structures that are induced from the various sources. Simultaneous estimation of the mean and correlation parameters is performed by alternating between the two systems of equations.

Item ID: 53773
Item Type: Article (Research - C1)
ISSN: 1467-9876
Keywords: generalized estimating equations, generalized method of moments, malaria, poisson model, spatial correlation
Funders: Singapore Management University, Australian Research Council (ARC)
Projects and Grants: ARC discovery grant DP130100766, ARC discovery grant DP160104292
Date Deposited: 05 Jun 2018 05:56
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010402 Biostatistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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