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

Author

Zhang, Xian-Da ; Zhang, Yuan-Sheng

Author_Institution

Changcheng Inst. of Metrol. & Meas., Beijing, China

Volume

41

Issue

8

fYear

1993

fDate

8/1/1993 12:00:00 AM

Firstpage

2657

Lastpage

2664

Abstract

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

Keywords

parameter estimation; signal processing; statistical analysis; ARMA models; MA order determination; SVD; autoregressive moving-average; cumulants; error matrix; higher-order statistics; nonGaussian processes; product of diagonal entries; rank determination; singular value decomposition; Additive noise; Autocorrelation; Gaussian noise; Gaussian processes; Higher order statistics; Phase estimation; Robustness; Spectral analysis; System identification; Testing;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.229896

Filename

229896