Ahmed El Ghini
aelghini@gmail.com
Estimation of moving average models generated by non Gaussian and dependent signals.
Estimation of the autoregressive moving average (ARMA) parameters of a stationary stochastic process
is a problem often encountered in the signal processing literature and computer vision. It's well known
that estimating the moving average (MA) parameters is usually more difficult than estimating the
autoregressive (AR) part, especially when the model is derived by non Gaussian and dependant
sequence. In this paper we present the MA parameter estimation algorithm given by the orthogonal
estimates of the inverse autocovariance function. The statistical properties of the algorithm are
explored and used to show that it is asymptotically efficient. The performance of the estimators is
evaluated via simulation and compared with the asymptotic theory. Joint work with Rémi Auguste,
Marius Bilasco and Chaabane Djeraba.