On moving average parameter estimation

Estimation of the autoregressive moving average (ARMA) parameters of a stationary stochastic process is a problem often encountered in the signal processing literature. It is well known that estimating the moving average (MA) parameters is usually more difficult than estimating the autoregressive (AR) part, especially if the zeros are located close to the unit circle. In this paper, we present four linear methods for MA parameter estimation (i.e., methods that involve only linear operations) and compare their performances first in a case when the zeros are located far away from the unit circle and secondly in a presumably harder case when the zeros are located very close to the unit circle.