Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation
Type
ArticleAuthors
Li, YuxiaoSun, Ying

KAUST Department
Statistics ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2018-10-08Online Publication Date
2018-10-08Print Publication Date
2019Embargo End Date
2019-10-08Permanent link to this record
http://hdl.handle.net/10754/656333
Metadata
Show full item recordAbstract
Spatial processes exhibit nonstationarity in many climate and environmental applications. Convolution-based approaches are often used to construct nonstationary covariance functions in Gaussian processes. Although convolution-based models are flexible, their computation is extremely expensive when the data set is large. Most existing methods rely on fitting an anisotropic, but stationary model locally, and then reconstructing the spatially varying parameters. In this study, we propose a new estimation procedure to approximate a class of nonstationary Matérn covariance functions by local-polynomial fitting the covariance parameters. The proposed method allows for efficient estimation of a richer class of nonstationary covariance functions, with the local stationary model as a special case. We also develop an approach for a fast high-resolution simulation with nonstationary features on a small scale and apply it to precipitation data in climate model outputs.Citation
Li, Y., & Sun, Y. (2019). Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation. Statistica Sinica. doi:10.5705/ss.202017.0536Sponsors
This research was supported by funding from King Abdullah University of Science and Technology (KAUST). We would like to thank the editor, associate editor, and two anonymous reviewers for their valuable comments.Publisher
Institute of Statistical ScienceJournal
Statistica SinicaAdditional Links
http://www3.stat.sinica.edu.tw/statistica/J29N3/J29N36/J29N36.htmlae974a485f413a2113503eed53cd6c53
10.5705/ss.202017.0536