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...
| Published in: | Automation and remote control Vol. 82, № 6. P. 1030-1048 |
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| Format: | Article |
| Language: | English |
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| Online Access: | http://vital.lib.tsu.ru/vital/access/manager/Repository/koha:000901385 Перейти в каталог НБ ТГУ |
| Summary: | 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. |
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| Bibliography: | Библиогр.: 29 назв. |
| ISSN: | 0005-1179 |