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{{One source|date=February 2022}}
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In [[statistics]] and [[signal processing]], the method of '''empirical orthogonal function''' ('''EOF''') analysis is a decomposition of a [[signal processing|signal]] or data set in terms of [[orthogonal]] [[basis function]]s which are determined from the data. The term is also interchangeable with the geographically weighted [[Principal components analysis
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* David B. Stephenson and Rasmus E. Benestad. [http://www.gfi.uib.no/~nilsg/kurs/notes/ "Environmental statistics for climate researchers"]. ''(See: [http://www.gfi.uib.no/~nilsg/kurs/notes/node87.html "Empirical Orthogonal Function analysis"])''
* Christopher K. Wikle and Noel Cressie. "[https://dx.doi.org/10.1093/biomet/86.4.815 A dimension reduced approach to space-time Kalman filtering]", ''[[Biometrika]]'' 86:815-829, 1999.
* Donald W. Denbo and John S. Allen. [http://journals.ametsoc.org/doi/pdf/10.1175/1520-0485(1984)014%3C0035%3AREOFAO%3E2.0.CO%3B2 "Rotary Empirical Orthogonal Function Analysis of Currents near the Oregon Coast"], "J. Phys. Oceanogr.", 14,
* David M. Kaplan [https://web.archive.org/web/20200701033210/https://websites.pmc.ucsc.edu/~dmk/notes/EOFs/EOFs.html] "Notes on EOF Analysis"
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