Optimal estimates of MA and ARMA parameters of non-Gaussian processes from high-order cumulants

Author

Porat, Boaz ; Friedlander, Benjamin

Author_Institution

Dept. of Electr. Eng., Technion, Haifa, Israel

fYear

1988

fDate

3-5 Aug 1988

Firstpage

208

Lastpage

212

Abstract

The authors describe an asymptotically minimum-variance algorithm for estimating the moving average (MA) and autoregressive moving average (ARMA) parameters of nonGaussian processes from sample second- and third-order moments. The algorithm uses the statistical properties (covariances and cross-covariances) of the sample moments explicitly. An alternative, simpler algorithm is also presented, which requires only linear operations. The latter algorithm is asymptotically minimum variance in the class of weighted least-squares algorithms

Keywords

parameter estimation; random processes; statistical analysis; ARMA; MA; asymptotically minimum variance; asymptotically minimum-variance algorithm; autoregressive moving average; covariances; cross-covariances; high-order cumulants; linear operations; moving average; nonGaussian processes; optimal estimates; statistical properties; weighted least-squares algorithms; Additive noise; Gaussian noise; H infinity control; Parameter estimation; Phase estimation; Recursive estimation; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Spectrum Estimation and Modeling, 1988., Fourth Annual ASSP Workshop on

Conference_Location

Minneapolis, MN

Type

conf

DOI

10.1109/SPECT.1988.206193

Filename

206193