Confidence estimation of autoregressive parameters based on noisy data

We consider the problem of estimating the parameters of an autoregressive process based on observations with additive noise. A sequential method has been developed for constructing a fixed-size confidence domain with a given confidence factor for a vector of unknown parameters based on a finite samp...

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Bibliographic Details
Published in:Automation and remote control Vol. 82, № 6. P. 1030-1048
Main Author: Konev, Victor V.
Other Authors: Pupkov, Andrey V.
Format: Article
Language:English
Subjects:
Online Access:http://vital.lib.tsu.ru/vital/access/manager/Repository/koha:000901385
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520 3 |a We consider the problem of estimating the parameters of an autoregressive process based on observations with additive noise. A sequential method has been developed for constructing a fixed-size confidence domain with a given confidence factor for a vector of unknown parameters based on a finite sample. Formulas are obtained for the duration of a procedure that achieves the required performance of estimates of unknown parameters in the case of Gaussian noise. Confidence parameter estimates are constructed using a special sequential modification of the classic Yule-Walker estimates; this permits one to estimate the confidence factor for small and moderate sample sizes. The results of numerical modeling of the proposed estimates are presented and compared with the Yule-Walker estimates using the example of confidence estimation of spectral density. 
653 |a авторегрессионные процессы 
653 |a аддитивный шум 
653 |a оценивание параметров 
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655 4 |a статьи в журналах  |9 879358 
700 1 |a Pupkov, Andrey V.  |9 809669 
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