Estimating autoregressive moving average model orders of non-Gaussian processes

In statistical signal processing, parametric modeling of non-Gaussian processes experiencing noise interference is a very important research area. The autoregressive moving average (ARMA) model is the most general and important tool of modeling system. This paper develops an algorithm for the selection of the proper ARMA model orders. The proposed technique is based on forming a third order cumulant matrix from the observed data sequence. The observed sequence is modeled as the output of an ARMA system that is excited by an unobservable input, and is corrupted by zero-mean Gaussian additive noise of unknown variance. Examples are given to demonstrate the performance of the proposed algorithm.