Finite Sample AIC for Autoregressive Model Order Selection

@article{Karimi2007FiniteSA,
  title={Finite Sample AIC for Autoregressive Model Order Selection},
  author={Mohsen. Karimi},
  journal={2007 IEEE International Conference on Signal Processing and Communications},
  year={2007},
  pages={1219-1222}
}
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… CONTINUE READING

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