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 ID32342
Article CategoryArticle
AuthorsHoque, Zahirul and Gerlach, Richard
Journal TitleJournal of Applied Statistical Science
Journal Citation17 (2), pp. 309-314
Number of Pages6
Year2005
ISSN1067-5817
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.

KeywordsDynamic model; Kalman filter; preliminary test estimator; shrinkage estimator; mean square error
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020490509. Statistical theory
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Byline AffiliationsUniversity of Newcastle
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