Asymptotically optimal estimation of MA and ARMA parameters of non-Gaussian processes from high-order moments

Author

Friedlander, Benjamin ; Porat, Boaz

Author_Institution

Signal Process. Technol. Ltd., Palo Alto, CA, USA

Volume

35

Issue

1

fYear

1990

fDate

1/1/1990 12:00:00 AM

Firstpage

27

Lastpage

35

Abstract

A description is given of an asymptotically-minimum-variance algorithm for estimating the MA (moving-average) and ARMA (autoregressive moving-average) parameters of non-Gaussian processes from sample high-order moments. The algorithm uses the statistical properties (covariances and cross covariances) of the sample moments explicitly. A simpler alternative algorithm that requires only linear operations is also presented. The latter algorithm is asymptotically-minimum-variance in the class of weighted least-squares algorithms

Keywords

parameter estimation; statistics; time series; ARMA parameters; asymptotically optimal estimation; asymptotically-minimum-variance algorithm; autoregressive moving-average; cross covariances; nonGaussian processes; parameter estimation; statistical properties; time series; weighted least-squares algorithms; Additive noise; Electronic switching systems; Gaussian noise; Gaussian processes; H infinity control; Parameter estimation; Parametric statistics; Phase estimation; Signal processing algorithms; Vectors;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.45140

Filename

45140