Show simple item record

dc.contributor.authorSmith, Keston W.  Concept link
dc.contributor.authorAretxabaleta, Alfredo L.  Concept link
dc.identifier.citationNonlinear Processes in Geophysics 14 (2007): 73-77en
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.publisherCopernicus Publications on behalf of the European Geosciences Union and the American Geophysical Unionen
dc.rightsAttribution-NonCommercial-ShareAlike 2.5 Generic*
dc.titleExpectation-maximization analysis of spatial time seriesen

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 2.5 Generic
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 2.5 Generic