Cart (Loading....) | Create Account
Close category search window

ARMA spectral estimation of time series with missing observations

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)

The problem of estimating the power spectral density of stationary time series when the measurements are not contiguous is considered. A new autoregressive moving-average (ARMA) method is proposed for this problem, based on nonlinear optimization of a weighted-squared-error criterion. The method can handle either regularly or randomly missing observations. As a special case, the method can handle the problem of missing sample covariances. The computational complexity is modest compared to exact maximum likelihood estimation of the same parameters. The performance of the algorithm is illustrated by some numerical examples and is shown to be statistically efficient in these cases.

Published in:

Information Theory, IEEE Transactions on  (Volume:30 ,  Issue: 6 )

Date of Publication:

Nov 1984

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.