A Hierarchical Bayesian Model for Spatial Prediction of Multivariate Non-Gaussian Random Fields
Article first published online: 30 MAR 2010
© 2010, The International Biometric Society
Volume 67, Issue 1, pages 97–105, March 2011
How to Cite
Chagneau, P., Mortier, F., Picard, N. and Bacro, J.-N. (2011), A Hierarchical Bayesian Model for Spatial Prediction of Multivariate Non-Gaussian Random Fields. Biometrics, 67: 97–105. doi: 10.1111/j.1541-0420.2010.01415.x
- Issue published online: 30 MAR 2010
- Article first published online: 30 MAR 2010
- Received February 2009. Revised January 2010. Accepted February 2010.
- Count data;
- Moving average;
- Ordinal data;
- Spatial prediction
Summary As most georeferenced data sets are multivariate and concern variables of different types, spatial mapping methods must be able to deal with such data. The main difficulties are the prediction of non-Gaussian variables and the modeling of the dependence between processes. The aim of this article is to present a new hierarchical Bayesian approach that permits simultaneous modeling of dependent Gaussian, count, and ordinal spatial fields. This approach is based on spatial generalized linear mixed models. We use a moving average approach to model the spatial dependence between the processes. The method is first validated through a simulation study. We show that the multivariate model has better predictive abilities than the univariate one. Then the multivariate spatial hierarchical model is applied to a real data set collected in French Guiana to predict topsoil patterns.