Collective spectral density estimation and clustering for spatially-correlated data
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ArticleAuthors
Chen, Tianbo
Sun, Ying

Maadooliat, Mehdi

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Statistics Program
Date
2020-05-16Online Publication Date
2020-05-16Print Publication Date
2020-08Embargo End Date
2022-05-16Submitted Date
2019-06-11Permanent link to this record
http://hdl.handle.net/10754/662866
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In 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.Citation
Chen, T., Sun, Y., & Maadooliat, M. (2020). Collective spectral density estimation and clustering for spatially-correlated data. Spatial Statistics, 100451. doi:10.1016/j.spasta.2020.100451Sponsors
The 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.Publisher
Elsevier BVJournal
Spatial StatisticsarXiv
2007.14085Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S2211675320300452ae974a485f413a2113503eed53cd6c53
10.1016/j.spasta.2020.100451