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＜departmental bulletin paper＞
On Parameter Estimation of Autoregressive Process in the Presence of Noise

Creator
Creator Name
Jia, Li-Juan
Affiliation
Affiliation Name
Department of Electrical and Electronic Systems Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University : Doctoral Student
Creator Name
Jin, Chun-Zhi
金, 春植
キン, シュンショク
Affiliation
Affiliation Name
Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University
九州大学大学院システム情報科学研究院電気電子システム工学部門
Creator Name
Yang, Zi-Jiang
楊, 子江
ヨウ, シコウ
Affiliation
Affiliation Name
Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University
九州大学大学院システム情報科学研究院電気電子システム工学部門
Creator Name
Wada, Kiyoshi
和田, 清
ワダ, キヨシ
Affiliation
Affiliation Name
Department of Electrical and Electronic Systems Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University
九州大学大学院システム情報科学研究院電気電子システム工学部門
Language
English
Publisher
九州大学大学院システム情報科学研究院
Faculty of Information Science and Electrical Engineering, Kyushu University
Date
2001-09-26
Source Title
Research reports on information science and electrical engineering of Kyushu University
Vol
6
Issue
2
First Page
185
Last Page
190
Publication Type
Version of Record
Access Rights
open access
JaLC DOI
https://doi.org/10.15017/1515734
Related DOI
https://portal.isee.kyushu-u.ac.jp/
Related URI
https://portal.isee.kyushu-u.ac.jp/
Relation
https://portal.isee.kyushu-u.ac.jp/
Abstract The paper studies the problem of parameter estimation for autoregressive (AR) process in the presence of white observation noise. A new type of bias compensated least-square (BCLS) algorithm is propos...ed to obtain consistent parameter estimate for AR models. The main feature of the proposed algorithm is that an auxiliary backward output parameter estimator is introduced in order to estimate the variance of observation noise. The proposed algorithm compensates the bias via the estimated variance of observation noise and hence yields a consistent parameter estimate. Some comments are given to illustrate that the proposed algorithm is less computational burden and more numerically reliable. Numerical results are provided to support these comments.show more

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Details

PISSN
1342-3819
EISSN
2188-0891
NCID
AN10569524
Record ID
1515734
Peer-Reviewed
Refereed
Subject Terms
Autoregressive process
Parameter estimation
Bias compensation
Asymptotic bias
Created Date 2015.05.25
Modified Date 2023.11.27

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