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dc.contributor.authorChen, Tianbo
dc.contributor.authorSun, Ying
dc.contributor.authorMaadooliat, Mehdi
dc.date.accessioned2020-05-19T07:03:15Z
dc.date.available2020-05-19T07:03:15Z
dc.date.issued2020-05-16
dc.date.submitted2019-06-11
dc.identifier.citationChen, T., Sun, Y., & Maadooliat, M. (2020). Collective spectral density estimation and clustering for spatially-correlated data. Spatial Statistics, 100451. doi:10.1016/j.spasta.2020.100451
dc.identifier.issn2211-6753
dc.identifier.doi10.1016/j.spasta.2020.100451
dc.identifier.urihttp://hdl.handle.net/10754/662866
dc.description.abstractIn this paper, we develop a method for estimating and clustering two-dimensional spectral density functions (2D-SDFs) for spatial data from multiple subregions. We use a common set of adaptive basis functions to explain the similarities among the 2D-SDFs in a low-dimensional space and estimate the basis coefficients by maximizing the Whittle likelihood with two penalties. We apply these penalties to impose the smoothness of the estimated 2D-SDFs and the spatial dependence of the spatially-correlated subregions. The proposed technique provides a score matrix, that is comprised of the estimated coefficients associated with the common set of basis functions representing the 2D-SDFs. Instead of clustering the estimated SDFs directly, we propose to employ the score matrix for clustering purposes, taking advantage of its low-dimensional property. In a simulation study, we demonstrate that our proposed method outperforms other competing estimation procedures used for clustering. Finally, to validate the described clustering method, we apply the procedure to soil moisture data from the Mississippi basin to produce homogeneous spatial clusters. We produce animations to dynamically show the estimation procedure, including the estimated 2D-SDFs and the score matrix, which provide an intuitive illustration of the proposed method.
dc.description.sponsorshipThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) to Ying Sun and Tianbo Chen. We would also like to thank the editor, and two referees for their constructive and thoughtful comments which helped us tremendously in improving the manuscript.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S2211675320300452
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, [, , (2020-05-16)] DOI: 10.1016/j.spasta.2020.100451 . © 2020. 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.titleCollective spectral density estimation and clustering for spatially-correlated data
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.identifier.journalSpatial Statistics
dc.rights.embargodate2022-05-16
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Mathematical and Statistical Sciences, Marquette University, USA.
dc.identifier.pages100451
dc.identifier.arxivid2007.14085
kaust.personChen, Tianbo
kaust.personSun, Ying
dc.date.accepted2020-05-10
dc.date.published-online2020-05-16
dc.date.published-print2020-08


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