Skip to main content

On Estimation and Prediction for Spatial Generalized Linear Mixed Models

Buy Article:

$51.00 plus tax (Refund Policy)



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.

Keywords: EM algorithm; Generalized linear mixed models; Metropolis-Hastings algorithm; Spatial interpolation; Variogram

Document Type: Research Article


Affiliations: Program in Statistics, Washington State University, Pullman, Washington 99164-3144, U.S.A., Email:

Publication date: March 1, 2002

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Partial Open Access Content
Partial Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more