Non-Asymptotic Confidence Estimation of the Autoregressive Process Paremeters in the Case of an Unknown Noise Variance

IF 0.5 Q4 PHYSICS, MULTIDISCIPLINARY

Optoelectronics Instrumentation and Data Processing Pub Date : 2024-01-12 DOI:10.3103/s8756699023040106

S. E. Vorobeychikov, A. V. Pupkov

引用次数: 0

Abstract

A non-asymptotic procedure of constructing the confidence region of the parameter of the \(p\)-th order Gaussian autoregressive process AR(p) in the case of an unknown process noise variance is considered. The confidence estimation procedure is based on the martingale property of the numerator of estimate deviation of the least square technique (LST). The results of numerical modeling are presented.

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未知噪声方差情况下自回归过程帕雷米特的非渐近可信度估计

摘要在未知过程噪声方差的情况下，考虑了构建 \(p\)-th 阶高斯自回归过程 AR(p)参数置信区域的非渐近过程。置信度估计程序基于最小平方技术（LST）估计偏差分子的马氏特性。文中给出了数值建模的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Optoelectronics Instrumentation and Data Processing PHYSICS, MULTIDISCIPLINARY-

CiteScore

1.00

自引率

50.00%

发文量

期刊介绍： The scope of Optoelectronics, Instrumentation and Data Processing encompasses, but is not restricted to, the following areas: analysis and synthesis of signals and images; artificial intelligence methods; automated measurement systems; physicotechnical foundations of micro- and optoelectronics; optical information technologies; systems and components; modelling in physicotechnical research; laser physics applications; computer networks and data transmission systems. The journal publishes original papers, reviews, and short communications in order to provide the widest possible coverage of latest research and development in its chosen field.