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

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2 Author(s)
Friedlander, Benjamin ; Signal Process. Technol. Ltd., Palo Alto, CA, USA ; Porat, B.

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

Published in:

Automatic Control, IEEE Transactions on  (Volume:35 ,  Issue: 1 )

Date of Publication:

Jan 1990

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