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A Numerical Study of Entropy and Residual Entropy Estimators Based on Smooth Density Estimators for Non-negative Random Variables


A Numerical Study of Entropy and Residual Entropy Estimators Based on Smooth Density Estimators for Non-negative Random Variables

Chaubey, Yogendra P. ORCID: https://orcid.org/0000-0002-0234-1429 and Vu, Nhat Linh (2020) A Numerical Study of Entropy and Residual Entropy Estimators Based on Smooth Density Estimators for Non-negative Random Variables. Technical Report. Concordia University, Montreal. (Submitted)

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In this paper, we are interested in the entropy of a non-negative random variable. Since the underlying probability density function is unknown, we propose the use of Poisson smoothed histogram density estimator in order to estimate the entropy. To study the performance of our estimator, we run simulations on a wide range of densities and compare our entropy estimators with the existing estimators that based on different approaches such as spacing estimators. Furthermore, we extend our study to residual entropy estimators which is the entropy of a random variable given that it has been survived up to time t.

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Monograph (Technical Report)
Authors:Chaubey, Yogendra P. and Vu, Nhat Linh
Series Name:Technical Report Series,Dep. of Mathematics and Statistics
Institution:Concordia Univeristy
Date:3 March 2020
  • Natural Sciences and Engineering Research Council of Canada (NSERC)
Identification Number:Technical Report No. 1/20, March 2020
Keywords:Entropy estimator, information theory, residual entropy, density estimator, survival function.
ID Code:986481
Deposited On:21 Apr 2020 19:24
Last Modified:23 Apr 2020 18:22


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