Undersmoothed kernel entropy estimators

被引：19
|
作者：
Paninski, Liam [1 ]
Yajima, Masanao [1 ]
机构：
[1] Columbia Univ, Dept Stat, New York, NY 10027 USA
来源：
IEEE TRANSACTIONS ON INFORMATION THEORY | 2008年 / 54卷 / 09期
基金：
美国国家科学基金会;
关键词：
approximation theory; bias; consistency; density estimation; distribution-free bounds;
D O I：
10.1109/TIT.2008.928251
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We develop a "plug-in" kernel estimator for the differential entropy that is consistent even if the kernel width tends to zero as quickly as 1/N, where N is the number of independent and identically distributed (i.i.d.) samples. Thus, accurate density estimates are not required for accurate kernel entropy estimates; in fact, it is a good idea when estimating entropy to sacrifice some accuracy in the quality of the corresponding density estimate.
引用
收藏
页码：4384 / 4388
页数：5
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