@article {Zhang:March 2002:0006-341X:129,
	author = "Zhang H.",
	title = "On Estimation and Prediction for Spatial Generalized Linear Mixed Models",
	journal = "Biometrics",
	volume = "58",
	year = "March 2002",
	abstract = "Summary. <P></P>We use spatial generalized linear mixed models (GLMM) to model non-Gaussian spatial variables that are observed at sampling locations in a continuous area. In many applications, prediction of random effects in a spatial GLMM is of great practical interest. We show that the minimum mean-squared error (MMSE) prediction can be done in a linear fashion in spatial GLMMs analogous to linear kriging. We develop a Monte Carlo version of the EM gradient algorithm for maximum likelihood estimation of model parameters. A by-product of this approach is that it also produces the MMSE estimates for the realized random effects at the sampled sites. This method is illustrated through a simulation study and is also applied to a real data set on plant root diseases to obtain a map of disease severity that can facilitate the practice of precision agriculture.",
	pages = "129-136(8)",
	url = "http://www.ingentaconnect.com/content/bpl/biom/2002/00000058/00000001/art00015"
	doi = "doi:10.1111/j.0006-341X.2002.00129.x"
}