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Handling spuriosity in the Kalman filter

Author

Listed:
  • Lin, Dennis K. J.
  • Guttman, Irwin

Abstract

The Kalman filter, which is in popular use in various branches of engineering, is essentially a least squares procedure. One well-recognized concern in this least squares procedure is its non-robustness to spuriously generated observations that give rise to outlying observations, rendering the Kalman filter unstable, with devastating consequences in some situations. Much evidence exists that data almost always contain a small proportion of spuriously generated observations, and indeed, one wild observation can make the Kalman filter unstable. To handle this, we introduce a new recursive estimation scheme which is found to be robust to spurious observations. Examples are given to illustrate the new scheme.

Suggested Citation

  • Lin, Dennis K. J. & Guttman, Irwin, 1993. "Handling spuriosity in the Kalman filter," Statistics & Probability Letters, Elsevier, vol. 16(4), pages 259-268, March.
    • Handle: RePEc:eee:stapro:v:16:y:1993:i:4:p:259-268
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    Citations

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    Cited by:

    1. Guttman, Irwin & Lin, Dennis K. J., 1995. "Robust recursive estimation for correlated observations," Statistics & Probability Letters, Elsevier, vol. 23(1), pages 79-92, April.
    2. Vamerson Schwingel Ribeiro & Joilson Dias, 2004. "Índice De Atividade Econômica: Os Modelos De Filtro De Kalman E Box-Jenkins Comparados," Anais do XXXII Encontro Nacional de Economia [Proceedings of the 32nd Brazilian Economics Meeting] 103, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    3. Wolfgang Lemke & Deutsche Bundesbank, 2006. "Term Structure Modeling and Estimation in a State Space Framework," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-28344-7, October.

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