Abstract
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 language | English |
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Title of host publication | New Developments in Applied Statistics |
Publisher | Nova Science Publishers, Inc. |
Pages | 319-330 |
Number of pages | 12 |
ISBN (Electronic) | 9781536117776 |
ISBN (Print) | 9781613246481 |
Publication status | Published - Jan 1 2012 |
Keywords
- Dynamicmodel
- Kalman filter
- Preliminary test estimator
- Shrink-age estimator and mean square error
ASJC Scopus subject areas
- General Mathematics
- General Physics and Astronomy