Nonparametric entropy estimation based on randomly censored data

A. Carbonez, L. Gyorfi, E. C. van der Meulen

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Abstract

The Shannon entropy of a random variable X with density function f(x) is defined as H(f) = - ∫ f(x)log f(x) dx. Based on randomly censored observations a nonparametric estimator for H(f) is proposed if H(f) is finite and is nonnegative. This entropy estimator is histogram-based in the sense that it involves a histogram-based density estimator fn constructed from the censored data. We prove the a. s. consistency of this estimator.

Original languageEnglish
Pages (from-to)441-451
Number of pages11
JournalProblems of control and information theory
Volume20
Issue number6
Publication statusPublished - Dec 1 1991

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ASJC Scopus subject areas

  • Engineering(all)

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