On estimation and prediction for spatial generalized linear mixed models.

Hao Zhang

doi:10.1111/j.0006-341x.2002.00129.x

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On estimation and prediction for spatial generalized linear mixed models.

Hao Zhang

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Abstract

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.

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Algorithms Computer Simulation Hordeum Models, Statistical Monte Carlo Method Mycoses Plant Diseases Plant Roots Predictive Value of Tests Journal Article

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Hao Zhang. 2002. On estimation and prediction for spatial generalized linear mixed models.. https://doi.org/10.1111/j.0006-341x.2002.00129.x

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