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The Complementary Model in Continuous/Discrete Smoothing

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Part of the book series: Lecture Notes in Statistics ((LNS,volume 25))

Abstract

We consider the problem of smoothing a continuous-time random process using irregularly spaced noisy samples. If the random process to be smoothed is generated by a linear state model, the relevant Hamiltonian system can be easily derived using the concept of complementary model, introduced by Weinert and Desai [1]. All smoothing algorithms can then be obtained via various changes of variables in the Hamiltonian system.

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References

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© 1984 Springer-Verlag Berlin Heidelberg

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Weinert, H.L. (1984). The Complementary Model in Continuous/Discrete Smoothing. In: Parzen, E. (eds) Time Series Analysis of Irregularly Observed Data. Lecture Notes in Statistics, vol 25. Springer, New York, NY. https://doi.org/10.1007/978-1-4684-9403-7_17

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  • DOI: https://doi.org/10.1007/978-1-4684-9403-7_17

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-96040-1

  • Online ISBN: 978-1-4684-9403-7

  • eBook Packages: Springer Book Archive

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