Optimal estimation from limited noisy data
β Scribed by A.G Ramm
- Publisher
- Elsevier Science
- Year
- 1987
- Tongue
- English
- Weight
- 330 KB
- Volume
- 125
- Category
- Article
- ISSN
- 0022-247X
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π SIMILAR VOLUMES
Two new types of bias-eliminated least-squares (BELS) based algorithms are proposed for consistent identiΓΏcation of linear systems with noisy input and output measurements. It is shown that estimation of the noise variances can be implemented through one-dimension over-parametrization of the system
We study differentiation of functions f based on noisy data f (t i )+= i . We recover f (k) either at a single point or on the interval [0, 1] in L 2 -norm. Under stochastic assumptions on f and = i , we determine the order of the errors of the best linear methods which use n noisy function values.