Close category search window
 

Empirical likelihood-based inference in multivariate fuzzy linear regression model

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)
Zhao Zhiwen ; Coll. of Math., Jilin Normal Univ., Siping, China ; Yang Li

In this paper, we consider the fuzzy linear regression model proposed by Kim et al. and construct empirical likelihood ratio statistics of unknown parameters. Under suitable conditions, we prove that the statistics converges in distribution to χ2(p+1). Using these results, we can construct the confidence region of unknown parameters.

Published in:
Industrial and Information Systems (IIS), 2010 2nd International Conference on  (Volume:2 )

Date of Conference: 10-11 July 2010

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 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.