Optimal Choice of AR and MA Parts in Autoregressive Moving Average Models

Abstract

This paper deals with the Bayesian method of choosing the best model for a given one-dimensional series among a finite number of candidates belonging to autoregressive (AR), moving average (MA), ARMA, and other families. The series could be either a sequence of observations in time as in speech applications, or a sequence of pixel intensities of a two-dimensional image. The observation set is not restricted to be Gaussian. We first derive an optimum decision rule for assigning the given observation set to one of the candidate models so as to minimize the average probability of error in the decision. We also derive an optimal decision rule so as to minimize the average value of the loss function. Then we simplify the decision rule when the candidate models are different Gaussian ARMA models of different orders. We discuss the consistency of the optimal decision rule and compare it with the other decision rules in the literature for comparing dynamical models.