{"title":"Chen-G类分布","authors":"Lea Anzagra, S. Sarpong, Suleman Nasiru","doi":"10.1080/25742558.2020.1721401","DOIUrl":null,"url":null,"abstract":"Abstract The quest to generate distributions with more desirable and flexible properties for the modeling of data has led to an intense focus on the development of new families that are generalizations of existing distributions by researchers. A new family of distributions called the chen generated family is developed in this study. Its statistical properties such as the quantile, moments, incomplete moments, stochastic ordering and order statistics are derived by using the method of maximum likelihood, estimators for the parameters of the new family are developed. Three special distributions, Chen Burr III, Chen Kumaraswamy and Chen Weibull, are proposed from the new family, though it can generalize other distributions. A demonstration of the usefulness of the new family is performed using real dataset.","PeriodicalId":92618,"journal":{"name":"Cogent mathematics & statistics","volume":" ","pages":""},"PeriodicalIF":0.1000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/25742558.2020.1721401","citationCount":"9","resultStr":"{\"title\":\"Chen-G class of distributions\",\"authors\":\"Lea Anzagra, S. Sarpong, Suleman Nasiru\",\"doi\":\"10.1080/25742558.2020.1721401\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The quest to generate distributions with more desirable and flexible properties for the modeling of data has led to an intense focus on the development of new families that are generalizations of existing distributions by researchers. A new family of distributions called the chen generated family is developed in this study. Its statistical properties such as the quantile, moments, incomplete moments, stochastic ordering and order statistics are derived by using the method of maximum likelihood, estimators for the parameters of the new family are developed. Three special distributions, Chen Burr III, Chen Kumaraswamy and Chen Weibull, are proposed from the new family, though it can generalize other distributions. A demonstration of the usefulness of the new family is performed using real dataset.\",\"PeriodicalId\":92618,\"journal\":{\"name\":\"Cogent mathematics & statistics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.1000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/25742558.2020.1721401\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cogent mathematics & statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/25742558.2020.1721401\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cogent mathematics & statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/25742558.2020.1721401","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 9
摘要
为了生成具有更理想和更灵活的数据建模属性的分布,研究人员强烈关注现有分布的新家族的发展。本文发展了一种新的分布族,称为陈生成族。利用极大似然方法推导了其分位数、矩量、不完全矩量、随机有序量和有序统计量等统计性质,并给出了新族参数的估计量。虽然它可以推广其他分布,但从新族中提出了三个特殊分布,Chen Burr III, Chen Kumaraswamy和Chen Weibull。使用真实数据集演示了新家族的有用性。
Abstract The quest to generate distributions with more desirable and flexible properties for the modeling of data has led to an intense focus on the development of new families that are generalizations of existing distributions by researchers. A new family of distributions called the chen generated family is developed in this study. Its statistical properties such as the quantile, moments, incomplete moments, stochastic ordering and order statistics are derived by using the method of maximum likelihood, estimators for the parameters of the new family are developed. Three special distributions, Chen Burr III, Chen Kumaraswamy and Chen Weibull, are proposed from the new family, though it can generalize other distributions. A demonstration of the usefulness of the new family is performed using real dataset.