Bias correction in least-squares identification
作者:
PETRE STOICA,
TORSTEN SÖDERSTRÖM,
期刊:
International Journal of Control
(Taylor Available online 1982)
卷期:
Volume 35,
issue 3
页码: 449-457
ISSN:0020-7179
年代: 1982
DOI:10.1080/00207178208922631
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
The least-squares method in system identification leads generally to biased parameter estimates. A conceptually simple modification is to estimate the bias and to compute compensated parameter estimates. When white output noise is the only disturbance this principle (compensated least squares, CLS) can readily be used to obtain consistent estimates. The main purpose of the paper is to investigate the accuracy of the parameter estimates obtained when the CLS method is used. It is proved that the estimates are asymptotically gaussian distributed. An explicit expression for the covariance matrix is given. It is also shown that a commonly used instrumental variable method gives (asymptotically) better accuracy than the compensated least-squares method.
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