Optimal network design for Bayesian spatial prediction of multivariate non-Gaussian environmental data

Abstract

This paper deals with the problem of increasing air pollution monitoring stations in Tehran city for efficient spatial prediction. As the data are multivariate and skewed, we introduce two multivariate skew models through developing the univariate skew Gaussian random field proposed by Zareifard and Jafari Khaledi [21]. These models provide extensions of the linear model of coregionalization for non-Gaussian data. In the Bayesian framework, the optimal network design is found based on the maximum entropy criterion. A Markov chain Monte Carlo algorithm is developed to implement posterior inference. Finally, the applicability of two proposed models is demonstrated by analyzing an air pollution data set.

Keywords:

• air pollution
• Bayesian sampling design
• entropy
• linear model of coregionalization
• multivariate spatial data
• skew Gaussian process

Acknowledgements

The author thank the associate editor and three reviewers for insightful and helpful comments.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by Shahid Beheshti University Research Council under the grant number 600-1400.