Improved Estimation in Linear Dynamic Regression Model

Article

Hoque, Zahirul and Gerlach, Richard. 2005. "Improved Estimation in Linear Dynamic Regression Model." Journal of Applied Statistical Science. 17 (2), pp. 309-314.

Article Title	Improved Estimation in Linear Dynamic Regression Model
ERA Journal ID	32342
Article Category	Article
Authors	Hoque, Zahirul and Gerlach, Richard
Journal Title	Journal of Applied Statistical Science
Journal Citation	17 (2), pp. 309-314
Number of Pages	6
Year	2005
ISSN	1067-5817
Abstract	This paper studies the preliminary test and shrinkage estimators of linear state space regression model via Kalman filtering. The performance of the estimators, with respect to mean square error, has been investigated. It has been revealed that under certain conditions both preliminary test and shrinkage estimators outperform Kalman filter but shrinkage estimator is superior to preliminary test estimator. Hence, the result presented in this paper invalidates the minimum mean square error property of Kalman filter that is widely used by the engineers for estimation of the parameters of linear state space models.
Keywords	Dynamic model; Kalman filter; preliminary test estimator; shrinkage estimator; mean square error
Contains Sensitive Content	Does not contain sensitive content
ANZSRC Field of Research 2020	490509. Statistical theory
Public Notes	Files associated with this item cannot be displayed due to copyright restrictions.
Byline Affiliations	University of Newcastle

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