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Evaluation of Methods for AR Coefficients Estimation Using Monte Carlo Analysis

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Date
2016
Author
Klejmova, Eva
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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 performance 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
Persistent identifier
http://hdl.handle.net/11012/83959
Document type
Peer reviewed
Document version
Final PDF
Source
Proceedings of the 22nd Conference STUDENT EEICT 2016. s. 375-379. ISBN 978-80-214-5350-0
http://www.feec.vutbr.cz/EEICT/
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  • Student EEICT 2016 [183]
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