A Note on the W-S Lower Bound of the MEE Estimation

The minimum error entropy (MEE) estimation is concerned with the estimation of a certain random variable (unknown variable) based on another random variable (observation), so that the entropy of the estimation error is minimized. This estimation method may outperform the well-known minimum mean squa...

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Main Authors: Badong Chen, Guangmin Wang, Nanning Zheng, Jose C. Principe
Format: Article
Language:English
Published: MDPI AG 2014-02-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/16/2/814
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author Badong Chen
Guangmin Wang
Nanning Zheng
Jose C. Principe
author_facet Badong Chen
Guangmin Wang
Nanning Zheng
Jose C. Principe
author_sort Badong Chen
collection DOAJ
description The minimum error entropy (MEE) estimation is concerned with the estimation of a certain random variable (unknown variable) based on another random variable (observation), so that the entropy of the estimation error is minimized. This estimation method may outperform the well-known minimum mean square error (MMSE) estimation especially for non-Gaussian situations. There is an important performance bound on the MEE estimation, namely the W-S lower bound, which is computed as the conditional entropy of the unknown variable given observation. Though it has been known in the literature for a considerable time, up to now there is little study on this performance bound. In this paper, we reexamine the W-S lower bound. Some basic properties of the W-S lower bound are presented, and the characterization of Gaussian distribution using the W-S lower bound is investigated.
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spelling doaj.art-0b90ef5a5d5c40c5b867639abab79ca32022-12-22T03:59:34ZengMDPI AGEntropy1099-43002014-02-0116281482410.3390/e16020814e16020814A Note on the W-S Lower Bound of the MEE EstimationBadong Chen0Guangmin Wang1Nanning Zheng2Jose C. Principe3Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, ChinaInstitute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, ChinaInstitute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, ChinaDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USAThe minimum error entropy (MEE) estimation is concerned with the estimation of a certain random variable (unknown variable) based on another random variable (observation), so that the entropy of the estimation error is minimized. This estimation method may outperform the well-known minimum mean square error (MMSE) estimation especially for non-Gaussian situations. There is an important performance bound on the MEE estimation, namely the W-S lower bound, which is computed as the conditional entropy of the unknown variable given observation. Though it has been known in the literature for a considerable time, up to now there is little study on this performance bound. In this paper, we reexamine the W-S lower bound. Some basic properties of the W-S lower bound are presented, and the characterization of Gaussian distribution using the W-S lower bound is investigated.http://www.mdpi.com/1099-4300/16/2/814estimationentropyMEE estimationW-S lower bound
spellingShingle Badong Chen
Guangmin Wang
Nanning Zheng
Jose C. Principe
A Note on the W-S Lower Bound of the MEE Estimation
Entropy
estimation
entropy
MEE estimation
W-S lower bound
title A Note on the W-S Lower Bound of the MEE Estimation
title_full A Note on the W-S Lower Bound of the MEE Estimation
title_fullStr A Note on the W-S Lower Bound of the MEE Estimation
title_full_unstemmed A Note on the W-S Lower Bound of the MEE Estimation
title_short A Note on the W-S Lower Bound of the MEE Estimation
title_sort note on the w s lower bound of the mee estimation
topic estimation
entropy
MEE estimation
W-S lower bound
url http://www.mdpi.com/1099-4300/16/2/814
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