Optimal sampling schedule for parameter estimation of linear models with unknown but bounded measurement errors

The problem of optimal sampling design for parameter estimation when data are generated by linear models is addressed. The measurements are assumed to be corrupted by an unknown but bounded additive noise. The sampling design assumes that the number of samples is unconstrained and no replication is...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 32; no. 2; pp. 179 - 182
Main Authors Belforte, G., Bona, B., Frediani, S.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.02.1987
Institute of Electrical and Electronics Engineers
Subjects
Additive noise
Applied sciences
Computer errors
Computer science; control theory; systems
Context modeling
Control theory. Systems
Exact sciences and technology
Measurement errors
Modelling and identification
Noise measurement
Parameter estimation
Particle measurements
Sampling methods
State estimation
Vectors
Optimal sampling
Error
Parameter estimation
Linear system
Limit
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Summary:The problem of optimal sampling design for parameter estimation when data are generated by linear models is addressed. The measurements are assumed to be corrupted by an unknown but bounded additive noise. The sampling design assumes that the number of samples is unconstrained and no replication is allowed. Two main results are shown: 1) for particular classes of linear models, the optimal number of measurements is equal to the number of parameters, as in the statistical context; 2) the uncertainty intervals of the parameter estimates are bounded from above by quantities that can be computer a priori, knowing only the model and the error structure.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.1987.1104535