{"title":"α-混合相关条件下Mathai-Haubold熵和残差Mathai-Haubold熵函数的核估计","authors":"R. Maya, M. Irshad","doi":"10.1080/01966324.2021.1935366","DOIUrl":null,"url":null,"abstract":"Abstract Mathai and Haubold introduced a new generalized entropy namely Mathai-Haubold entropy and Dar and Al-Zahrani proposed the Mathai-Haubold entropy for the residual life time and called it as residual Mathai-Haubold entropy. In the present paper, we propose nonparametric estimators for the Mathai-Haubold entropy and the residual Mathai-Haubold entropy where the observations under consideration are exhibiting α-mixing (strong mixing) dependence condition. Asymptotic properties of the estimators are established under suitable regular conditions. A Monte Carlo simulation study is carried out to compare the performance of the estimators using the mean squared error. The methods are illustrated using a real data set.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"41 1","pages":"148 - 159"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01966324.2021.1935366","citationCount":"2","resultStr":"{\"title\":\"Kernel Estimation of Mathai-Haubold Entropy and Residual Mathai-Haubold Entropy Functions under α-Mixing Dependence Condition\",\"authors\":\"R. Maya, M. Irshad\",\"doi\":\"10.1080/01966324.2021.1935366\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Mathai and Haubold introduced a new generalized entropy namely Mathai-Haubold entropy and Dar and Al-Zahrani proposed the Mathai-Haubold entropy for the residual life time and called it as residual Mathai-Haubold entropy. In the present paper, we propose nonparametric estimators for the Mathai-Haubold entropy and the residual Mathai-Haubold entropy where the observations under consideration are exhibiting α-mixing (strong mixing) dependence condition. Asymptotic properties of the estimators are established under suitable regular conditions. A Monte Carlo simulation study is carried out to compare the performance of the estimators using the mean squared error. The methods are illustrated using a real data set.\",\"PeriodicalId\":35850,\"journal\":{\"name\":\"American Journal of Mathematical and Management Sciences\",\"volume\":\"41 1\",\"pages\":\"148 - 159\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/01966324.2021.1935366\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Mathematical and Management Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/01966324.2021.1935366\",\"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/10.1080/01966324.2021.1935366","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Business, Management and Accounting","Score":null,"Total":0}
Kernel Estimation of Mathai-Haubold Entropy and Residual Mathai-Haubold Entropy Functions under α-Mixing Dependence Condition
Abstract Mathai and Haubold introduced a new generalized entropy namely Mathai-Haubold entropy and Dar and Al-Zahrani proposed the Mathai-Haubold entropy for the residual life time and called it as residual Mathai-Haubold entropy. In the present paper, we propose nonparametric estimators for the Mathai-Haubold entropy and the residual Mathai-Haubold entropy where the observations under consideration are exhibiting α-mixing (strong mixing) dependence condition. Asymptotic properties of the estimators are established under suitable regular conditions. A Monte Carlo simulation study is carried out to compare the performance of the estimators using the mean squared error. The methods are illustrated using a real data set.