Title :
Finite Sample AIC for Autoregressive Model Order Selection
Author_Institution :
Electr. Eng. Dept., Shiraz Univ., Shiraz, Iran
Abstract :
An estimate for the prediction error of the least-squares-forward (LSF) autoregressive (AR) parameter estimation method has been recently proposed. In this paper, this estimate is used for deriving a new AR model order selection criterion. This new criterion is an estimate of the Kullback-Leibler index and can replace the Akaike information criterion (AIC) and its corrected version AICC. In a simulation study, the performance of this new criterion and other existing order selection criteria is examined in the finite sample case. Simulation results show that the performance of the proposed criterion is much better than the other theoretically derived criteria.
Keywords :
autoregressive processes; least squares approximations; parameter estimation; prediction theory; Akaike information criterion; Kullback-Leibler index estimation; autoregressive model order selection; autoregressive parameter estimation; finite sample AIC; least-squares-forward; prediction error estimation; Autoregressive processes; Bayesian methods; Convergence; Costs; Information theory; Parameter estimation; Predictive models; Signal processing; Autoregressive processes; Information theory; Modeling;
Conference_Titel :
Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on
Conference_Location :
Dubai
Print_ISBN :
978-1-4244-1235-8
Electronic_ISBN :
978-1-4244-1236-5
DOI :
10.1109/ICSPC.2007.4728545
