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dc.contributor.authorSmith, Keston W.  Concept link
dc.contributor.authorAretxabaleta, Alfredo L.  Concept link
dc.date.accessioned2007-04-16T13:59:47Z
dc.date.available2007-04-16T13:59:47Z
dc.date.issued2007-02-01
dc.identifier.citationNonlinear Processes in Geophysics 14 (2007): 73-77en
dc.identifier.urihttps://hdl.handle.net/1912/1571
dc.description© Author(s) 2007. This work is licensed under a Creative Commons License. The definitive version was published in Nonlinear Processes in Geophysics 14 (2007): 73-77, doi: 10.5194/npg-14-73-2007en
dc.description.abstractExpectation maximization (EM) is used to estimate the parameters of a Gaussian Mixture Model for spatial time series data. The method is presented as an alternative and complement to Empirical Orthogonal Function (EOF) analysis. The resulting weights, associating time points with component distributions, are used to distinguish physical regimes. The method is applied to equatorial Pacific sea surface temperature data from the TAO/TRITON mooring time series. Effectively, the EM algorithm partitions the time series into El Nino, La Nina and normal conditions. The EM method leads to a clearer interpretation of the variability associated with each regime than the basic EOF analysis.en
dc.description.sponsorshipThis work was supported by NSF grant DMS-0417845.en
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherCopernicus Publications on behalf of the European Geosciences Union and the American Geophysical Unionen
dc.relation.urihttps://doi.org/10.5194/npg-14-73-2007
dc.rightsAttribution-NonCommercial-ShareAlike 2.5 Generic*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/*
dc.titleExpectation-maximization analysis of spatial time seriesen
dc.typeArticleen


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