On parameter estimation for noncausal models of non-Gaussian signals

The authors consider the problem of estimating the parameters of a stable (stationary), scalar ARMA (autoregressive moving average) (p , q) signal model driven by an i.i.d. (independently identically distributed) non-Gaussian sequence. The driving noise sequence is not observed. The signal is allowed to be nonminimum phase and/or noncausal (i.e., poles may lie both inside and outside the unit circle). The author addresses the problem of parameter identifiability given the higher order cumulants of the signal on a finite set of lags. The set of lags required to achieve parameter identifiability is the smallest to date. Two computer simulation examples are presented to illustrate the proposed approach