Constrained logarithmic least squares in parameter estimation

This paper first shows that while robust in terms of the average output error, the least squares estimate is sensitive to outliers with respect to the maximum output error. In fact the worst case output error of the least squares can go unbounded. Then, a constrained logarithmic least squares for sy...

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Bibliographic Details
Published in:IEEE transactions on automatic control 1999-01, Vol.44 (1), p.182-186
Main Authors: BAI, E.-W, YINYU YE
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Applied sciences
Cities and towns
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
Least squares approximation
Least squares methods
Minimax techniques
Modelling and identification
Noise measurement
Parameter estimation
Robustness
System identification
Transfer functions
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Summary:This paper first shows that while robust in terms of the average output error, the least squares estimate is sensitive to outliers with respect to the maximum output error. In fact the worst case output error of the least squares can go unbounded. Then, a constrained logarithmic least squares for system identification is proposed. Analytic center algorithms are presented to solve this constrained logarithmic least squares problem.
ISSN:0018-9286
1558-2523
DOI:10.1109/9.739123