Non-asymptotic Confidence Estimation of the Autoregressive Parameter in AR(1) Process by Noisy Observations
For parameter in an AR(1) process corrupted by noise, the paper proposes the construction of confidence interval for unknown parameter with a prescribed coverage probability. The noises both in observable and in unobservable processes are assumed to be Gaussian with unknown variance. The estimation procedure is nonasymptotic and uses a special stopping rule. The results of numerical simulation by Monte-Carlo method are presented. Contribution of the authors: the authors contributed equally to this article. The authors declare no conflicts of interests.
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Keywords
autoregressive process, non-asymptotic estimation, confidence intervalAuthors
| Name | Organization | |
| Vorobeychikov Sergey E. | Tomsk State University | sev@mail.tsu.ru |
| Pupkov Andrey V. | Tomsk State University | andrewpupkov@gmail.com |
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Non-asymptotic Confidence Estimation of the Autoregressive Parameter in AR(1) Process by Noisy Observations | Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitelnaja tehnika i informatika – Tomsk State University Journal of Control and Computer Science. 2022. № 59. DOI: 10.17223/19988605/59/9
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