GENERALIZED JENSEN DIFFERENCE DIVERGENCE MEASURES AND FISHER MEASURE OF INFORMATION

被引：2
|
作者：
PARDO, L
MORALES, D
TANEJA, IJ
机构：
来源：
KYBERNETES | 1995年 / 24卷 / 02期
关键词：
D O I：
10.1108/03684929510083066
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Fisher's amount of information is the most parametric measure in the literature of statistics. However, not for every family of probability density functions do the well-known regularity assumptions hold. To avoid this problem, several parametric measures have been proposed on the basis of divergence measures. In this work, parametric measures of information are obtained on the basis of the generalized Jensen difference divergence measures. When the regularity assumptions hold, their relations with Fisher's amount of information are also studied.
引用
收藏
页码：15 / 28
页数：14
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