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Bayesian nonparametric spatial modeling with dirichlet process mixing
Customary modeling for continuous point-referenced data assumes a Gaussian process that is often taken to be stationary. When such models are fitted within a Bayesian framework, the unknown parameters of the process are assumed to be random, so a random Gaussian process results. Here we propose a novel spatial Dirichlet process mixture model to produce a random spatial process that is neither Gaussian nor stationary. We first develop a spatial Dirichlet process model for spatial data and discuss its properties. Because of familiar limitations associated with direct use of Dirichlet process models, we introduce mixing by convolving this process with a pure error process. We then examine properties of models created through such Dirichlet process mixing. In the Bayesian framework, we implement posterior inference using Gibbs sampling. Spatial prediction raises interesting questions, but these can be handled. Finally, we illustrate the approach using simulated data, as well as a dataset involving precipitation measurements over the Languedoc-Roussillon region in southern France. © 2005 American Statistical Association.
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- Statistics & Probability
- 4905 Statistics
- 3802 Econometrics
- 1603 Demography
- 1403 Econometrics
- 0104 Statistics
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![Journal cover image](https://secure.syndetics.com/index.aspx?isbn=/lc.gif&issn=0162-1459&client=dukeuniv)
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 3802 Econometrics
- 1603 Demography
- 1403 Econometrics
- 0104 Statistics