Non-asymptotic Confidence Estimation of the Autoregressive Parameter in AR(1) Process with an Unknown Noise Variance

Non-asymptotic Confidence Estimation of the Autoregressive Parameter in AR(1) Process with an Unknown Noise Variance

Bibliographic Details
Parent link:Austrian Journal of Statistics
Vol. 49, № 4 : Special Issue CDAM 2019.— 2020.— [P. 19-26]
Main Author: Vorobeychikov S. E. Sergey Erikovich
Corporate Author: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Other Authors: Burkatovskaya Yu. B. Yuliya Borisovna
Summary:Title screen
The paper considers the estimation problem of the autoregressive parameter in the first-order autoregressive process with Gaussian noises when the noise variance is unknown. We propose a non-asymptotic technique to compensate the unknown variance, and then, to construct a point estimator with any prescribed mean square accuracy. Also a fixed-width confidence interval with any prescribed coverage accuracy is proposed. The results of Monte-Carlo simulations are given.
Published: 2020
Subjects:
электронный ресурс
труды учёных ТПУ
autoregressive process
non-asymptotic estimation
confidence interval
авторегрессионные процессы
неассоциативная алгебра
доверительные интервалы
Online Access:https://doi.org/10.17713/ajs.v49i4.1121
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663397

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