Improved estimation for dynamic linear regression model

    Research output: Chapter in Book/Report/Conference proceedingChapter


    This paper studies the preliminary test and shrinkage estimators based on the Kalman filtering procedure applied to a dynamic linear state space regression model. The per- formance of these estimators, with respect to mean square error, was investigated. It was revealed that under certain conditions both the preliminary test and shrinkage esti- mators proposed outperformthe Kalman filter. This out-performance was not uniform. Further, the shrinkage estimator was found to be superior to the preliminary test es- timator over large regions. The results presented in this paper invalidates the global minimum mean square error property of the Kalman filter that is widely used by the engineers for estimation of the parameters of linear state space models.

    Original languageEnglish
    Title of host publicationNew Developments in Applied Statistics
    PublisherNova Science Publishers, Inc.
    Number of pages12
    ISBN (Electronic)9781536117776
    ISBN (Print)9781613246481
    Publication statusPublished - Jan 1 2012


    • Dynamicmodel
    • Kalman filter
    • Preliminary test estimator
    • Shrink-age estimator and mean square error

    ASJC Scopus subject areas

    • Mathematics(all)
    • Physics and Astronomy(all)


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