{"title":"长度偏差和I型滤波存在下香农熵的估计","authors":"R. Rajesh, R. G., S. Sunoj","doi":"10.1080/01966324.2021.1941452","DOIUrl":null,"url":null,"abstract":"Abstract Length-biased data appear when sampling lifetimes by cross-section. This article presents a nonparametric kernel estimators of entropy function for the length-biased sample under type I censoring. We have shown that the proposed estimator is consistent and asymptotically normal under suitable regularity conditions. We have conducted simulation studies to assess the performance of the proposed estimators.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01966324.2021.1941452","citationCount":"1","resultStr":"{\"title\":\"Estimation of Shannon Entropy in the Presence of Length-Bias and Type I Censoring\",\"authors\":\"R. Rajesh, R. G., S. Sunoj\",\"doi\":\"10.1080/01966324.2021.1941452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Length-biased data appear when sampling lifetimes by cross-section. This article presents a nonparametric kernel estimators of entropy function for the length-biased sample under type I censoring. We have shown that the proposed estimator is consistent and asymptotically normal under suitable regularity conditions. We have conducted simulation studies to assess the performance of the proposed estimators.\",\"PeriodicalId\":35850,\"journal\":{\"name\":\"American Journal of Mathematical and Management Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/01966324.2021.1941452\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Mathematical and Management Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/01966324.2021.1941452\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Business, Management and Accounting\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Mathematical and Management Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/01966324.2021.1941452","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Business, Management and Accounting","Score":null,"Total":0}
Estimation of Shannon Entropy in the Presence of Length-Bias and Type I Censoring
Abstract Length-biased data appear when sampling lifetimes by cross-section. This article presents a nonparametric kernel estimators of entropy function for the length-biased sample under type I censoring. We have shown that the proposed estimator is consistent and asymptotically normal under suitable regularity conditions. We have conducted simulation studies to assess the performance of the proposed estimators.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
