{"title":"过去熵的二元扩张","authors":"G. Rajesh, E. I. Abdul-Sathar, K. V. Reshmi","doi":"10.29252/jirss.19.1.185","DOIUrl":null,"url":null,"abstract":". Di Crescenzo and Longobardi (2002) has been proposed a measure of uncertainty related to past life namely past entropy. The present paper addresses the question of extending this concept to bivariate set-up and study some properties of the proposed measure. It is shown that the proposed measure uniquely determines the distribution function. Characterizations for some bivariate lifetime models are obtained using the proposed measure. Further, we define new classes of life distributions based on this measure and properties of the new classes are also discussed. We also proposed a non-parametric kernel estimator for the proposed measure and illustrated performance of the estimator using a numerical data. 62G30; 62E10, 62B10.","PeriodicalId":42965,"journal":{"name":"JIRSS-Journal of the Iranian Statistical Society","volume":" ","pages":""},"PeriodicalIF":0.1000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bivariate Extension of Past Entropy\",\"authors\":\"G. Rajesh, E. I. Abdul-Sathar, K. V. Reshmi\",\"doi\":\"10.29252/jirss.19.1.185\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". Di Crescenzo and Longobardi (2002) has been proposed a measure of uncertainty related to past life namely past entropy. The present paper addresses the question of extending this concept to bivariate set-up and study some properties of the proposed measure. It is shown that the proposed measure uniquely determines the distribution function. Characterizations for some bivariate lifetime models are obtained using the proposed measure. Further, we define new classes of life distributions based on this measure and properties of the new classes are also discussed. We also proposed a non-parametric kernel estimator for the proposed measure and illustrated performance of the estimator using a numerical data. 62G30; 62E10, 62B10.\",\"PeriodicalId\":42965,\"journal\":{\"name\":\"JIRSS-Journal of the Iranian Statistical Society\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.1000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JIRSS-Journal of the Iranian Statistical Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29252/jirss.19.1.185\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JIRSS-Journal of the Iranian Statistical Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29252/jirss.19.1.185","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
摘要
Di Crescenzo和Longobardi(2002)提出了一种与过去生活有关的不确定性度量,即过去熵。本文讨论了将这一概念推广到二元设置的问题,并研究了所提出的测度的一些性质。结果表明,所提出的测度唯一地确定了分布函数。使用所提出的测度获得了一些双变量寿命模型的特征。此外,我们基于这一测度定义了新的生命分布类别,并讨论了新类别的性质。我们还为所提出的测度提出了一个非参数核估计器,并使用数值数据说明了该估计器的性能。62G30;62E10、62B10。
. Di Crescenzo and Longobardi (2002) has been proposed a measure of uncertainty related to past life namely past entropy. The present paper addresses the question of extending this concept to bivariate set-up and study some properties of the proposed measure. It is shown that the proposed measure uniquely determines the distribution function. Characterizations for some bivariate lifetime models are obtained using the proposed measure. Further, we define new classes of life distributions based on this measure and properties of the new classes are also discussed. We also proposed a non-parametric kernel estimator for the proposed measure and illustrated performance of the estimator using a numerical data. 62G30; 62E10, 62B10.