Analysis of spatial data with a nested correlation structure

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. 2017 Royal Statistical SocietyWe thank the Associate Editor and the referees for their perceptive comments and suggestions, that have led to a greatly improved version of this paper. Denis Leung�s research is funded by the Research Center at Singapore Management University. You-Gan Wang�s research is funded by Australian Research Council discovery grant DP130100766 and project DP160104292.Scopu