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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