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A Kalman Filter for a Poisson Series with Covariates and Laplace Approximation Integration

dc.contributor.authorGaver, Donald Paul
dc.contributor.authorJacobs, Patricia A.
dc.date1991-09
dc.date.accessioned2013-03-07T21:53:06Z
dc.date.available2013-03-07T21:53:06Z
dc.date.issued1991-09
dc.identifier.urihttp://hdl.handle.net/10945/29941
dc.description.abstractA hierarchical model for a Poisson time series is introduced. The model allows the mean or rate of the Poisson variables to vary slowly in time; it is modeled as the exponential of an AR/1 process. In addition the rate is influenced by a covariate. The Laplace method is used to recursively update some model parameter estimates. Frankly heuristic methods are explored to estimate other of the underlying parameters. The methodology is checked against simulated data with encouraging resultsen_US
dc.description.sponsorshipNaval Postgraduate School, Monterey, CAen_US
dc.description.urihttp://archive.org/details/kalmanfilterforp00gave
dc.language.isoen_US
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.lcshKALMAN FILTERING.en_US
dc.titleA Kalman Filter for a Poisson Series with Covariates and Laplace Approximation Integrationen_US
dc.typeTechnical Reporten_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentOperations Research
dc.subject.authorPoisson time series with covariates; hierarchical model; Kalman filter; Laplace methoden_US
dc.description.funderO&MN, Direct Fundingen_US
dc.description.recognitionNAen_US
dc.identifier.oclcNA
dc.identifier.npsreportNPS-OR-91-030


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