Abstract

We generalize the Mixture Periodic Autoregressive (MPAR) model introduced by Shao to the Mixture Periodic Autoregressive Moving Average (MPARMA) model for the modelling nonlinear time series. The stationarity is derived. The estimation is done via EM algorithm and the model selection criterion is given. The model is illustrated by analyzing the particulate matter concentrations in Cleveland, OH.

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