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dc.contributor.authorHuang, Huang
dc.contributor.authorSun, Ying
dc.date.accessioned2017-11-29T11:13:57Z
dc.date.available2017-11-29T11:13:57Z
dc.date.issued2017-11-23
dc.identifier.citationHuang H, Sun Y (2017) Visualization and assessment of spatio-temporal covariance properties. Spatial Statistics. Available: http://dx.doi.org/10.1016/j.spasta.2017.11.004.
dc.identifier.issn2211-6753
dc.identifier.doi10.1016/j.spasta.2017.11.004
dc.identifier.urihttp://hdl.handle.net/10754/626249
dc.description.abstractSpatio-temporal covariances are important for describing the spatio-temporal variability of underlying random fields in geostatistical data. For second-order stationary random fields, there exist subclasses of covariance functions that assume a simpler spatio-temporal dependence structure with separability and full symmetry. However, it is challenging to visualize and assess separability and full symmetry from spatio-temporal observations. In this work, we propose a functional data analysis approach that constructs test functions using the cross-covariances from time series observed at each pair of spatial locations. These test functions of temporal lags summarize the properties of separability or symmetry for the given spatial pairs. We use functional boxplots to visualize the functional median and the variability of the test functions, where the extent of departure from zero at all temporal lags indicates the degree of non-separability or asymmetry. We also develop a rank-based nonparametric testing procedure for assessing the significance of the non-separability or asymmetry. Essentially, the proposed methods only require the analysis of temporal covariance functions. Thus, a major advantage over existing approaches is that there is no need to estimate any covariance matrix for selected spatio-temporal lags. The performances of the proposed methods are examined by simulations with various commonly used spatio-temporal covariance models. To illustrate our methods in practical applications, we apply it to real datasets, including weather station data and climate model outputs.
dc.description.sponsorshipThe authors thank the reviewers for their valuable comments that greatly improved the quality of this article. This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award OSR-2015-CRG4-2582. The authors also thank the North American Regional Climate Change Assessment Program (NARCCAP) for providing the data used in this paper. NARCCAP is funded by the National Science Foundation (NSF), the U.S. Department of Energy (DoE), the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Environmental Protection Agency Office of Research and Development (EPA) .
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S2211675317301306
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 22 November 2017. DOI: 10.1016/j.spasta.2017.11.004. © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectFull symmetry
dc.subjectFunctional boxplot
dc.subjectFunctional data ranking
dc.subjectRank-based test
dc.subjectSeparability
dc.subjectSpatio-temporal covariance
dc.titleVisualization and assessment of spatio-temporal covariance properties
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalSpatial Statistics
dc.eprint.versionPost-print
dc.identifier.arxivid1705.01789
kaust.personHuang, Huang
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
dc.versionv1
refterms.dateFOA2019-11-22T00:00:00Z
dc.date.published-online2017-11-23
dc.date.published-print2017-11
dc.date.posted2017-05-04


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