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SPATIO-TEMPORAL MODELS FOR SOME DATA SETS IN CONTINUOUS SPACE AND DISCRETE TIME

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dc.citation.doi10.5705/ss.2013.223w
dc.citation.epage98
dc.citation.issn1017-0405
dc.citation.issue1
dc.citation.jtitleStatistica Sinica
dc.citation.spage81
dc.citation.volume25
dc.contributor.authorDemel, S. S.
dc.contributor.authorDu, Juan
dc.contributor.authoreiddujuan
dc.date.accessioned2016-04-06T15:21:29Z
dc.date.available2016-04-06T15:21:29Z
dc.date.published2015
dc.descriptionCitation: Demel, S. S., & Du, J. (2015). SPATIO-TEMPORAL MODELS FOR SOME DATA SETS IN CONTINUOUS SPACE AND DISCRETE TIME. Statistica Sinica, 25(1), 81-98. doi:10.5705/ss.2013.223w
dc.descriptionSpace time data sets are often collected at monitored discrete time lags, which are usually viewed as a component of time series. Valid and practical covariance structures are needed to model these types of data sets in various disciplines, such as environmental science, climatology, and agriculture. In this paper we propose several classes of spatio-temporal functions whose discrete temporal margins are some celebrated autoregressive and moving average (ARMA) models, and obtain necessary and sufficient conditions for them to be valid covariance functions. The possibility of taking advantage of well-established time series and spatial statistics tools makes it relatively easy to identify and fit the proposed model in practice. A spatio-temporal model with moving average type of temporal margin is fitted to Kansas daily precipitation to illustrate the application of the proposed model comparing with some popular spatio-temporal models in literature.
dc.description.embargoEmbargo 1/1/2016
dc.identifier.urihttp://hdl.handle.net/2097/32479
dc.relation.urihttps://doi.org/10.5705/ss.2013.223w
dc.rights.urihttp://www.sherpa.ac.uk/romeo/issn/1017-0405/
dc.subjectAutoregressive And Moving Average Process
dc.subjectFourier Transform
dc.subjectMatern
dc.subjectCovariance Function
dc.subjectSpatio-Temporal Covariance Function
dc.subjectStationary Covariance Functions
dc.titleSPATIO-TEMPORAL MODELS FOR SOME DATA SETS IN CONTINUOUS SPACE AND DISCRETE TIME
dc.typeArticle

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