An application of Bayesian variable selection to spatial concurrent linear models

Zuofeng Shang, Murray K. Clayton

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Spatial concurrent linear models, in which the model coefficients are spatial processes varying at a local level, are flexible and useful tools for analyzing spatial data. One approach places stationary Gaussian process priors on the spatial processes, but in applications the data may display strong nonstationary patterns. In this article, we propose a Bayesian variable selection approach based on wavelet tools to address this problem. The proposed approach does not involve any stationarity assumptions on the priors, and instead we impose a mixture prior directly on each wavelet coefficient. We introduce an option to control the priors such that high resolution coefficients are more likely to be zero. Computationally efficient MCMC procedures are provided to address posterior sampling, and uncertainty in the estimation is assessed through posterior means and standard deviations. Examples based on simulated data demonstrate the estimation accuracy and advantages of the proposed method. We also illustrate the performance of the proposed method for real data obtained through remote sensing.

Original languageEnglish (US)
Pages (from-to)521-544
Number of pages24
JournalEnvironmental and Ecological Statistics
Issue number4
StatePublished - Dec 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • General Environmental Science
  • Statistics, Probability and Uncertainty


  • Bayesian estimation
  • Blockwise Gibbs sampler
  • Haar wavelet
  • Inference
  • Mixture prior
  • Satellite images
  • Spike and slab prior


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