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dc.contributor.authorWikle, Christopher K., 1963-eng
dc.contributor.otherUniversity of Missouri-Columbia. College of Arts and Sciences. Department of Statisticseng
dc.date.issued2002eng
dc.descriptionThis is the pre-print version of the article found in Statistical Modelling (http://smj.sagepub.com/).eng
dc.description.abstractSpatio-temporal processes can often be written as hierarchical state-space processes. In situations with complicated dynamics such as wave propagation, it is difficult to parameterize state transition functions for high-dimensional state processes. Although in some cases prior understanding of the physical process can be used to formulate models for the state transition, this is not always possible. Alternatively, for processes where one considers discrete time and continuous space, complicated dynamics can be modeled by stochastic integro-difference equations in which the associated redistribution kernel is allowed to vary with space and/or time. By considering a spectral implementation of such models, one can formulate a spatio-temporal model with relatively few parameters that can accommodate complicated dynamics. This approach can be developed in a hierarchical framework for non-Gaussian processes, as demonstrated on cloud intensity data.eng
dc.description.sponsorshipThis research was made possible by a grant from the U.S. Environmental Protection Agency's Science to Achieve Results (STAR) program, Assistance Agreement No. R827257-01-0.eng
dc.identifier.citationStatistical Modelling, 2, pp. 299-314.eng
dc.identifier.urihttp://hdl.handle.net/10355/9074eng
dc.publisherStatistical Modellingeng
dc.relation.ispartofStatistics publications (MU)eng
dc.subjectBayesianeng
dc.subjectdynamic modelseng
dc.subjectdilationeng
dc.subject.lcshStatistics -- Modelseng
dc.subject.lcshBayesian statistical decision theoryeng
dc.titleA Kernel-Based Spectral Model for Non-Gaussian Spatio-Temporal Processeseng
dc.typePreprinteng


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