Identification and estimation of non-Gaussian ARMA processes

Author

Lii, Keh-Shin

Author_Institution

Dept. of Stat., California Univ., Riverside, CA, USA

Volume

38

Issue

7

fYear

1990

fDate

7/1/1990 12:00:00 AM

Firstpage

1266

Lastpage

1276

Abstract

A method to identify and estimate non-Gaussian autoregressive moving average (ARMA) processes which uses bispectral analysis and the Pade approximation is presented. It is shown that the method will consistently identify the order of the ARMA model and estimate the parameters of the model. Various asymptotic distributions are given to facilitate the model identification and parameter estimation. A few examples are presented to illustrate the effectiveness of the method. The procedure is modified to handle the case when there is additive Gaussian noise. The modified procedure is asymptotically consistent in the estimation of orders and parameters of the ARMA model when Gaussian noise is present

Keywords

parameter estimation; random noise; spectral analysis; ARMA processes; Gaussian noise; Pade approximation; asymptotic distributions; autoregressive moving average; bispectral analysis; identification; nonGaussian processes; parameter estimation; Additive noise; Autoregressive processes; Density functional theory; Frequency response; Gaussian noise; Gaussian processes; Maximum likelihood estimation; Parameter estimation; Predictive models; Statistical distributions;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.57555

Filename

57555