NONPARAMETRIC ENTROPY ESTIMATION BASED ON RANDOMLY CENSORED-DATA

被引：0
|
作者：
CARBONEZ, A
GYORFI, L
VANDERMEULEN, EC
机构：
[1] KATHOLIEKE UNIV LEUVEN,DEPT MATH,B-3001 HEVERLEE,BELGIUM
[2] TECH UNIV BUDAPEST,H-1521 BUDAPEST,HUNGARY
来源：
PROBLEMS OF CONTROL AND INFORMATION THEORY-PROBLEMY UPRAVLENIYA I TEORII INFORMATSII | 1991年 / 20卷 / 06期
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The Shannon entropy of a random variable X with density function f(x) is defined as H(f) = - integral 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 f(n) constructed from the censored data. We prove the a. s. consistency of this estimator.
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页码：441 / 451
页数：11
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