Identification and estimation of non-Gaussian ARMA processes

A method to identify and estimate non-Gaussian autoregressive moving average (ARMA) processes which uses bispectral analysis and the Pade approximation is presented. It is shown that the method will consistently identify the order of the ARMA model and estimate the parameters of the model. Various asymptotic distributions are given to facilitate the model identification and parameter estimation. A few examples are presented to illustrate the effectiveness of the method. The procedure is modified to handle the case when there is additive Gaussian noise. The modified procedure is asymptotically consistent in the estimation of orders and parameters of the ARMA model when Gaussian noise is present. >

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