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A Note on the W-S Lower Bound of the MEE Estimation
AbstractThe 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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Chen, B.; Wang, G.; Zheng, N.; Principe, J.C. A Note on the W-S Lower Bound of the MEE Estimation. Entropy 2014, 16, 814-824.View more citation formats
Chen B, Wang G, Zheng N, Principe JC. A Note on the W-S Lower Bound of the MEE Estimation. Entropy. 2014; 16(2):814-824.Chicago/Turabian Style
Chen, Badong; Wang, Guangmin; Zheng, Nanning; Principe, Jose C. 2014. "A Note on the W-S Lower Bound of the MEE Estimation." Entropy 16, no. 2: 814-824.