Non-parametric estimation of Kullback-Leibler discrimination information based on censored data

被引：1

作者：

Sathar, Abdul E., I ^{[1
]}

Viswakala, K. V. ^{[1
]}

机构：

[1] Univ Kerala, Dept Stat, Thiruvananthapuram 695581, Kerala, India

来源：

STATISTICS & PROBABILITY LETTERS | 2019年 / 154卷

关键词：

Kullback Leibler discrimination information; Non parametric estimation; Kernel density estimation; Mean squared error (MSE); Mean integrated squared error (MISE); TESTING EXPONENTIALITY; LINDLEY DISTRIBUTION; RESIDUAL LIFE; DENSITY;

D O I：

10.1016/j.spl.2019.06.007

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Kullback-Leibler discrimination information is the most well-known theoretic divergence measure between two probability distributions associated with the same experiment, which finds application in the field of information theory In the present paper, we propose non-parametric estimators for the Kullback-Leibler discrimination information for the lifetime distribution based on censored data. Asymptotic properties of the estimators are established under suitable regularity conditions. Monte-Carlo simulation studies are carried out to compare the performance of the estimators based on the mean-squared error. The method is illustrated using a real data set. (C) 2019 Elsevier B.V. All rights reserved.

引用

页数：9

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