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Threshold bounds in SVD and a new iterative algorithm for order selection in AR models

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1 Author(s)
K. Konstantinides ; Hewlett-Packard Lab., Palo Alto, CA, USA

The problem of order determination of AR (autoregressive) models using singular value decomposition (SVD) is reexamined from a statistical point of view. Thresholds for distinguishing between significant and nonsignificant singular values are derived, and a novel iterative algorithm for order selection in AR models is presented. Simulation results show the technique to be very effective when a small number of samples is available

Published in:

IEEE Transactions on Signal Processing  (Volume:39 ,  Issue: 5 )