Skip to main content

On Estimation and Prediction for Spatial Generalized Linear Mixed Models

Buy Article:

$43.00 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.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

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: [email protected]

Publication date: 2002-03-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed 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