Skip to main content

On Estimation and Prediction for Spatial Generalized Linear Mixed Models

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

Summary.

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

DOI: http://dx.doi.org/10.1111/j.0006-341X.2002.00129.x

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

Publication date: March 1, 2002

bpl/biom/2002/00000058/00000001/art00015
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect 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