Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 5:00 PM ET (12:00 - 21:00 UTC). We apologize for the inconvenience.
By Topic

On the residual variance and the prediction error for the LSF estimation method and new modified finite sample criteria for autoregressive model order selection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Karimi, M. ; Electr. Eng. Dept., Shiraz Univ., Iran

The case where the data sample size is finite and the least-squares-forward (LSF) method is used for autoregressive (AR) parameter estimation is considered. New formulas describing the residual variance and the prediction error behaviors in AR parameter estimation are derived, and the relation between the residual variance and the prediction error is determined. Based on this relation, the existing finite sample criteria for AR model order selection are modified, and it is shown that these modified criteria have better performance.

Published in:

Signal Processing, IEEE Transactions on  (Volume:53 ,  Issue: 7 )