Recursive nonlinear estimation of random parameter AR models with Poisson observations

TitleRecursive nonlinear estimation of random parameter AR models with Poisson observations
Publication TypeConference Paper
Year of Publication1997
AuthorsEvans, J. S., and V. Krishnamurthy
Conference NameDecision and Control, 1997., Proceedings of the 36th IEEE Conference on
Pagination5042 -5047 vol.5
Date Publisheddec.
Keywordsautoregressive processes, discrete time Poisson process, doubly stochastic AR process, exact filters, filtering theory, Gauss-Markov process, Markov processes, observers, Poisson observations, random parameter AR models, recursive estimation, recursive nonlinear estimation, sufficient statistic
Abstract

We derive exact filters for the state of a doubly stochastic AR process with parameters which vary according to a nonlinear function of a Gauss-Markov process. The observations consist of a discrete time Poisson process with rate a positive function of the Gauss-Markov process. The dimension of the sufficient statistic increases linearly with the number of observed events

URLhttp://dx.doi.org/10.1109/CDC.1997.649860
DOI10.1109/CDC.1997.649860

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