Autoregressive model orders for Durbin´s MA and ARMA estimators

Author

Broersen, P.M.T.

Author_Institution

Dept. of Appl. Phys., Delft Univ. of Technol., Netherlands

Volume

48

Issue

8

fYear

2000

fDate

8/1/2000 12:00:00 AM

Firstpage

2454

Lastpage

2457

Abstract

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

Keywords

autoregressive moving average processes; autoregressive processes; moving average processes; parameter estimation; sampling methods; ARMA parameters; Durbin´s methods; MA parameters; autoregressive model orders; autoregressive-moving average parameters; linear regression theory; long AR model; moving average parameters; parameter estimation; predicting AR model; sample size; Computational modeling; Estimation theory; Linear regression; Maximum likelihood estimation; Minimization methods; Parameter estimation; Physics; Poles and zeros; Predictive models; Yield estimation;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.852025

Filename

852025