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Estimating the Order of an Autoregressive Model Using Normalized Maximum Likelihood

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2 Author(s)
Daniel F. Schmidt ; Centre for MEGA Epidemiology, The University of Melbourne, Carlton, Australia ; Enes Makalic

This paper examines the estimation of the order of an autoregressive model using the minimum description length principle. A closed form for an approximation of the parametric complexity of the autoregressive model class is derived by exploiting a relationship between coefficients and partial autocorrelations. The parametric complexity over the complete parameter space is found to diverge. A model selection criterion is subsequently derived by bounding the parameter space, and simulations suggest that it compares well against standard autoregressive order selection techniques in terms of correct order identification and prediction error.

Published in:

IEEE Transactions on Signal Processing  (Volume:59 ,  Issue: 2 )