Abstract.The problem of estimating the order of autoregressive models is considered from the point of view of ranking and selection procedures. This approach offers a formulation to many problems more realistic than that of classical hypothesis testing or of criteria based on estimation theory (e.g., AIC). In the method considered here, sampling variations are taken into account and the experimenter is also allowed to incorporate any a priori knowledge of the true order (e.g., lower bound as well as upper bound).
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