Generalized spatial stick-breaking processes

Abstract

This paper develops a Bayesian nonparametric model for skewed spatial data with nonstationary dependence structure. A transformed Gaussian model is proposed for the atoms of the kernel stick-breaking process by transforming the margins of a Gaussian process to flexible marginal distributions. This study proves that the correlation structure of the underlying spatial process is nonstationary. Results from both simulated and real datasets demonstrate that the proposed model possesses better spatial prediction performance and offers computational advantages compared to the Bayesian nonparametric model with the Gaussian base measure.

Keywords:

• Copula models
• Non-Gaussian models
• Spatial statistics
• Stick-breaking process

MATHEMATICAL SUBJECT CLASSIFICATION:

• 62H11
• 62F15
• 60E05

Acknowledgments

We thank the reviewer and the editor for their valuable comments. Their very much precise comments have substantially improved the manuscript.