On the existence of autoregressive models for third-order cumulant matching

Author

Raghuveer, M.R. ; Dianat, Soheil A.

Author_Institution

Dept. of Electr. Eng., Rochester Inst. of Technol., NY, USA

Volume

37

Issue

12

fYear

1989

fDate

12/1/1989 12:00:00 AM

Firstpage

1931

Lastpage

1938

Abstract

Necessary and sufficient conditions are provided for the third-moment sequence of a white-noise-driven finite-order AR (autoregressive) model to match given samples of the third-moment sequence of an arbitrary stationary process. The conditions lead to a set of nonlinear equations that are solved for the model parameters. A method for finding the third-moment sequence of a white-noise-driven AR model from its parameters is also provided. One of the key results is that, unlike a finite set of autocorrelation samples, a finite set of third-moment sequence samples is not always linearly extendable to an infinite third-moment sequence

Keywords

filtering and prediction theory; spectral analysis; arbitrary stationary process; autoregressive models; filtering; model parameters; nonlinear equations; spectral analysis; third-moment sequence; third-order cumulant matching; white-noise-driven finite-order; Additive noise; Autocorrelation; Data mining; Filters; Gaussian noise; Nonlinear equations; Random processes; Spectral analysis; Sufficient conditions; White noise;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.45539

Filename

45539