By Topic

Autoregressive model orders for Durbin's MA and ARMA estimators

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
$33 $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)
Broersen, P.M.T. ; Dept. of Appl. Phys., Delft Univ. of Technol., Netherlands

Durbin's methods (1959, 1960) for moving average (MA) and autoregressive-moving average (ARMA) estimation use the parameters of a long AR model to compute the MA parameters. Linear regression theory is applied to find the best AR order. This yields two different orders: one for the best predicting AR model and another one for the long AR model with the best parameter accuracy, as intermediate for Durbin's estimates. Both orders increase with the sample size and have no finite limiting value

Published in:

Signal Processing, IEEE Transactions on  (Volume:48 ,  Issue: 8 )