DocumentCode :
3267676
Title :
The performance comparison of optimally weighted LS and linear minimum variance estimation of linear model with random input
Author :
Zhu, Yunmin ; Zhao, Juan ; Li, X. Rong
Author_Institution :
Dept. of Math., Sichuan Univ., Chengdu, China
Volume :
4
fYear :
2002
fDate :
10-13 Dec. 2002
Firstpage :
4258
Abstract :
The performance comparison of the optimally weighted LS estimate and the linear minimum variance estimate for a linear model with random input is presented. In this case optimally weighted LS estimate is not a linear estimate of a parameter given input and observation anymore while linear minimum variance estimate still is. Under a certain conditions on variance matrix invertibility, we show that the optimally weighted LS estimate outperforms the linear minimum variance estimates provided that they have the same a priori information on the parameter being estimated.
Keywords :
estimation theory; least squares approximations; linear systems; modelling; linear minimum variance estimation; linear model; optimally weighted least squares; performance comparison; random input; variance matrix invertibility; Ear; Least squares approximation; OWL; Parameter estimation; Production; Recursive estimation; State estimation; Statistics; Temperature; Vehicle driving;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 2002, Proceedings of the 41st IEEE Conference on
ISSN :
0191-2216
Print_ISBN :
0-7803-7516-5
Type :
conf
DOI :
10.1109/CDC.2002.1185039
Filename :
1185039
Link To Document :
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