By Topic

An efficient algorithm for calculating the likelihood and likelihood gradient of ARMA models

Sign In

Full text access may be available.

To access full text, please use your member or institutional sign in.

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)
D. Burshtein ; Dept. of Electr. Eng.-Syst., Tel-Aviv Univ., Israel

Exact analytical expressions are obtained for the likelihood and likelihood gradient stationary autoregressive moving average (ARMA) models. Denote the sample size by N, the autoregressive order by p, and the moving average order by q. The calculation of the likelihood requires (p+2q+1)N +o(N) multiply-add operations, and the calculation of the likelihood gradient requires (2p+6q+2)N+o(N) multiply-add operations. These expressions may be used to obtain an iterative, Newton-Raphson-type converging algorithm, with superlinear convergence rate, that computes the maximum-likelihood estimator in (2 p+6q+2)N+o(N) multiply-add operations per iteration

Published in:

IEEE Transactions on Automatic Control  (Volume:38 ,  Issue: 2 )