Noncausal Nonminimum Phase Arma Modeling Of Non-gaussian Processes

A method is presented for the estimation of the parameters of a noncausal nonminimum phase ARMA model for non-Gaussian random processes. Using certain higher-order cepstra slices, the Fourier phases of two intermediate sequences which are composed of the minimum phase parts of the AR and MA models and their maximum phase parts, respectively, can be reconstructed. Under the condition that the AR and MA models do not have common zeros, these twosequences can be estimated from their phases only, and lead to the reconstruction of the AR and MA parameters, within a scalar and a time shift. Through simulations it is shown that the estimation procedure is more unlike to lead to model order mismatch when compared to existing methods, and is fairly robust when a small order mismatch occurs.