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On An Entropy Estimator Based On a Non-parametric Density Estimator For Non-negative Data


On An Entropy Estimator Based On a Non-parametric Density Estimator For Non-negative Data

Chaubey, Yogendra and Vu, Nhat Linh (2020) On An Entropy Estimator Based On a Non-parametric Density Estimator For Non-negative Data. Technical Report. UNSPECIFIED. (Unpublished)

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In the recent decades, entropy has become more and more essential in statistics and machine learning. It features in many applications involving data transmission, cryptography, signal processing, network theory, bio-informatics, and so on. A large number of estimators for entropy have been proposed in the past ten years. Here we focus on entropy estimation for non-negative random variables. Specifically, the use of entropy estimator based on Poisson-weights density estimator is found to be of interest. We establish some asymptotic properties of the resulting estimators and present a simulation study comparing these with well known estimators in literature.

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Monograph (Technical Report)
Authors:Chaubey, Yogendra and Vu, Nhat Linh
Date:28 April 2020
  • Natural Sciences and Engineering Research Council
Keywords:information theory, entropy estimator, non-parametric density estimator, asymptotic properties.
ID Code:986758
Deposited On:07 May 2020 13:59
Last Modified:07 May 2020 13:59


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