Precision of yule-walker methods for the arma spectral model
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Abstract
In this work a new method is proposed of separated estimation for the ARMA spectral model based on the modified Yule-Walker equations and on the least squares method. The proposal of the new method consists of performing an AR filtering in the random process generated obtaining a new random estimate, which will reestimate the ARMA model parameters, given a better spectrum estimate. Some numerical examples will be presented in order to ilustrate the performance of the method proposed, which is evaluated by the relative error and the average variation coefficient.
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ARMA spectral estimation, Bootstrap, Digital Signal Processing, Least squares, Asymptotic stability, Computer simulation, Digital signal processing, Fourier transforms, Functions, Least squares approximations, Mathematical models, Matrix algebra, Maximum likelihood estimation, Monte Carlo methods, Random processes, Vectors, Autoregressive moving average (ARMA), Regression analysis
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English
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Proceedings of the IASTED International Conference on Circuits, Signals, and Systems, p. 54-59.
