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

Information Theory and Mixing Least-Squares Regressions

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)
Gilbert Leung ; Qualcomm Inc., Campbell, CA ; Barron, A.R.

For Gaussian regression, we develop and analyze methods for combining estimators from various models. For squared-error loss, an unbiased estimator of the risk of the mixture of general estimators is developed. Special attention is given to the case that the component estimators are least-squares projections into arbitrary linear subspaces, such as those spanned by subsets of explanatory variables in a given design. We relate the unbiased estimate of the risk of the mixture estimator to estimates of the risks achieved by the components. This results in simple and accurate bounds on the risk and its estimate, in the form of sharp and exact oracle inequalities. That is, without advance knowledge of which model is best, the resulting performance is comparable to or perhaps even superior to what is achieved by the best of the individual models. Furthermore, in the case that the unknown parameter has a sparse representation, our mixture estimator adapts to the underlying sparsity. Simulations show that the performance of these mixture estimators is better than that of a related model-selection estimator which picks a model with the highest weight. Also, the connection between our mixtures with Bayes procedures is discussed

Published in:

Information Theory, IEEE Transactions on  (Volume:52 ,  Issue: 8 )

Date of Publication:

Aug. 2006

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.