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KLEJMOVÁ, E.
Original Title
Evaluation of methods for AR coefficients estimation using monte carlo analysis
Type
article in a collection out of WoS and Scopus
Language
English
Original Abstract
Aim of this paper is to give recommendation for work with methods used for estimation of coefficients of autoregressive process. We applied Monte Carlo simulations to investigate perfor-mance of Burg, Yule-Walker and covariance methods. Evaluation of precision of spectral estimation is done with focus on signal length and lag order. The results are presented in graphical form and briefly discussed. Taking these results into account, Yule-Walker method shows better performance in case of long length signals and in case of overvalued lag order. Burg and covariance methods provide better results in case of short length signal and undervalued lag order.
Keywords
Autoregressive process, AIC, Burg method, Yule-Walker method, covariance method
Authors
Released
28. 4. 2016
Publisher
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Location
Brno
ISBN
978-80-214-5350-0
Book
Proceedings of the 22nd Conference STUDENT EEICT 2016
Edition number
1
Pages from
375
Pages to
379
Pages count
5
BibTex
@inproceedings{BUT124486, author="Eva {Klejmová}", title="Evaluation of methods for AR coefficients estimation using monte carlo analysis", booktitle="Proceedings of the 22nd Conference STUDENT EEICT 2016", year="2016", number="1", pages="375--379", publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií", address="Brno", isbn="978-80-214-5350-0" }