利用分位数函数的外性可靠性及其相关测度

IF 1.6 Q1 STATISTICS & PROBABILITY
A. S. Krishnan, S. Sunoj, P. Sankaran
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引用次数: 1

摘要

熵是信息测度家族的新成员,作为香农熵的补充对偶,用于测量随机变量的概率分布中包含的不确定性。概率分布既可以用分布函数来表示,也可以用分位数函数来表示。在许多应用工作中,没有可处理的分布函数,但分位数函数是存在的,在这种情况下,研究基于分位数的外向性是很重要的。因此,本文着重于利用分位数函数推导出外向性的一些性质及其相关测度。给出了基于分位数的残差熵的一些排序关系。引入了阶统计量的分位数外向性和累积外向性,并研究了其性质。通过模拟和实际数据分析,研究了基于分位数的外向性经验估计的一些应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Some Reliability Properties of Extropy and its Related Measures Using Quantile Function
Extropy is a recent addition to the family of information measures as a complementary dual of Shannon entropy, to measure the uncertainty contained in a probability distribution of a random variable. A probability distribution can be specified either in terms of the distribution function or by the quantile function. In many applied works, there do not have any tractable distribution function but the quantile function exists, where a study on the quantile-based extropy are of importance. The present paper thus focuses on deriving some properties of extropy and its related measures using quantile function. Some ordering relations of quantile-based residual extropy are presented. We also introduce the quantile-based extropy of order statistics and cumulative extropy and studied its properties. Some applications of empirical estimation of quantile-based extropy using simulation and real data analysis are investigated.
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来源期刊
Statistica
Statistica STATISTICS & PROBABILITY-
CiteScore
1.70
自引率
0.00%
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0
审稿时长
10 weeks
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