On Estimation and Prediction for Spatial Generalized Linear Mixed Models

Author: Zhang H.¹

Source: Biometrics, Volume 58, Number 1, March 2002 , pp. 129-136(8)

Publisher: Blackwell Publishing

Key:

- Free Content

- New Content

- Subscribed Content

- Free Trial Content

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

Affiliations: 1: Program in Statistics, Washington State University, Pullman, Washington 99164-3144, U.S.A., Email: zhanghao@wsu.edu

The full text electronic article is available for purchase. You will be able to download the full text electronic article after payment.

$41.72 plus tax Refund Policy