Smoothness priors in time series

dc.contributor.authorGersch, Will
dc.contributor.authorKitagawa, Genshiro
dc.contributor.corporateNaval Postgraduate School (U.S.)en_US
dc.contributor.departmentOperations Research (OR)en_US
dc.dateApril 1987
dc.date.accessioned2019-10-04T15:54:16Z
dc.date.available2019-10-04T15:54:16Z
dc.date.issued1987-04
dc.description.abstractA variety of time series signal extraction/smoothing problems are considered from a Bayesian “smoothness priors” point of view. The origin of the subject is a smoothing problem posed by Whittaker (1923). Using a stochastic regression-linear model-Gaussian disturbances framework, we model stationary time series and nonstationary mean and nonstationary covariance time series. Smoothness priors distributions on the model parameters are expressed either in terms of time domain stochastic difference equation or frequency domain constraints A small number of (hyper)parameters specify very complex time series behavior. The critical computation is the likelihood of the Bayesian model. Finally we show a smoothness priors state space-not necessarily Gaussian-not necessarily linear model of nonstationary time series.en_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.
dc.format.extent60 p.en_US
dc.identifier.npsreportNPS55-87-004en_US
dc.identifier.urihttps://hdl.handle.net/10945/63334
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.subject.authorBayesian modelen_US
dc.subject.authorsmoothness priorsen_US
dc.subject.authornon Gaussian time seriesen_US
dc.subject.authorstationary time seriesen_US
dc.subject.authornonstationery time seriesen_US
dc.titleSmoothness priors in time seriesen_US
dc.typeTechnical Reporten_US
dspace.entity.typePublication
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