ESTIMATION OF LINEAR PARAMETRIC MODELS USING INVERSE FILTER CRITERIA AND HIGHER-ORDER STATISTICS

We consider the problem of estimating the parameters of a stable, scalar ARMA (p, q) signal model (causal or noncausal, minimum phase or mixed phase) driven by an i.i.d. non-Gaussian sequence. The driving noise sequence is not observed. We analyze and extend the Wiggins-Donoho class of inverse filter criteria for estimation of model parameters. These criteria have been considered in the past only for moving average inverse filters. We extend these criteria to general ARMA inverses. Computer simulation examples are presented to illustrate the proposed approaches.