By Topic

Computation of the Fisher information matrix for SISO models

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

2 Author(s)
Klein, A. ; Dept. of Econ. Stat., Amsterdam Univ., Netherlands ; Melard, G.

Closed form expressions and an algorithm for obtaining the Fisher information matrix of Gaussian single input single output (SISO) time series models are presented. It enables the computation of the asymptotic covariance matrix of maximum likelihood estimators of the parameters. The procedure makes use of the autocovariance function of one or more autoregressive processes. Under certain conditions, the SISO model can be a special case of a vector autoregressive moving average (ARMA) model, for which there is a method to evaluate the Fisher information matrix. That method is compared with the procedure described in the paper

Published in:

Signal Processing, IEEE Transactions on  (Volume:42 ,  Issue: 3 )