 Modeling spatial data using local likelihood estimation and a Matérn to spatial autoregressive translationAshton Wiens, Douglas Nychka and William Kleiber Environmetrics, 2020, vol. 31, issue 6 Abstract: Modeling data with nonstationary covariance structure is important to represent heterogeneity in geophysical and other environmental spatial processes. In this work, we investigate a two‐stage approach to modeling nonstationary covariances that is efficient for large data sets. First, maximum likelihood estimation is used in local, moving windows to infer spatially varying covariance parameters. These surfaces of covariance parameters are then encoded into a global covariance model specifying the second‐order structure for the complete spatial domain. From this second step, the resulting global model allows for efficient simulation and prediction. This work uses a nonstationary spatial autoregressive (SAR) model, related to Gaussian Markov random field methods, as the global model which is amenable to plug in local estimates and practical for large datasets. A simulation study is used to establish the accuracy of local Matérn parameter estimation as a reliable technique for small window sizes and a modest number of replicated fields. This modeling approach is implemented on a nonstationary climate model dataset with the goal of emulating the variation in the numerical model ensemble using a Gaussian process. Date: 2020 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:envmet:v:31:y:2020:i:6:n:e2652 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Environmetrics from John Wiley & Sons, Ltd. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.