We propose to model a spatio-temporal random field that has nonstationary covariance structure in both space and time domains by applying the concept of the dimension expansion method in Bornn et al. (2012). Simulations are conducted for both separable and nonseparable space-time covariance models, and the model is also illustrated with a streamflow dataset. Both simulation and data analyses show that modeling nonstationarity in both space and time can improve the predictive performance over stationary covariance models or models that are nonstationary in space but stationary in time.
- Award ID(s):
- 1916208
- NSF-PAR ID:
- 10485005
- Publisher / Repository:
- Taylor and Francis
- Date Published:
- Journal Name:
- Journal of Computational and Graphical Statistics
- Volume:
- 31
- Issue:
- 4
- ISSN:
- 1061-8600
- Page Range / eLocation ID:
- 1025 to 1036
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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