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Singular value decomposition-based MA order determination of non-Gaussian ARMA models

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2 Author(s)
Xian-Da Zhang ; Changcheng Inst. of Metrol. & Meas., Beijing, China ; Zhang, Y.-S.

Singular-value-decomposition (SVD)-based moving-average (MA) order determination of non-Gaussian processes using higher-order statistics is addressed. It is shown that the MA order determination of autoregressive moving-average (ARMA) models is equivalent to the rank determination of a certain error matrix, and a SVD approach is proposed. Its simplified form is applied to pure MA models. To improve the robustness of the order selection, a combination of the SVD and the product of diagonal entries (PODE) test is proposed. Some interesting applications of the two SVD approaches are presented, and simulations verify their performance

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Signal Processing, IEEE Transactions on  (Volume:41 ,  Issue: 8 )