Analysis of spatial data with a nested correlation structure

Journal article

Adegboye, Oyelola, Leung, Denis 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. https://doi.org/10.1111/rssc.12230
AuthorsAdegboye, Oyelola, Leung, Denis and Wang, You-Gan
Abstract

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.

KeywordsGeneralized estimating equations; Generalized method of moments; Malaria; Poisson model; Spatial correlation
Year01 Jan 2018
JournalJournal of the Royal Statistical Society Series C: Applied Statistics
Journal citation67 (2), pp. 329-354
PublisherBlackwell Publishing Ltd
ISSN0035-9254
Digital Object Identifier (DOI)https://doi.org/10.1111/rssc.12230
Web address (URL)https://rss.onlinelibrary.wiley.com/doi/10.1111/rssc.12230
Open accessPublished as non-open access
Research or scholarlyResearch
Page range329-354
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All rights reserved
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Output statusPublished
Publication dates
Print20 Jul 2017
Publication process dates
Deposited06 Jan 2023
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© 2017 Royal Statistical Society

Place of publicationUnited Kingdom
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