Ishfaq S. Ahmad, Rameesa Jan, Poonam Nirwan, Peer Bilal Ahmad
{"title":"A New Class of Distribution Over Bounded Support and Its Associated Regression Model","authors":"Ishfaq S. Ahmad, Rameesa Jan, Poonam Nirwan, Peer Bilal Ahmad","doi":"10.1007/s40745-023-00483-3","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, a new two-parameter distribution over the bounded support (0,1) is introduced and studied in detail. Some of the interesting statistical properties like concavity, hazard rate function, mean residual life, moments and quantile function are discussed. The method of moments and maximum likelihood estimation methods are used to estimate unknown parameters of the proposed model. Besides, finite sample performance of estimation methods are evaluated through the Monte-Carlo simulation study. Application of the proposed distribution to the real data sets shows a better fit than many known two-parameter distributions on the unit interval. Moreover, a new regression model as an alternative to various unit interval regression models is introduced.\n</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Data Science","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s40745-023-00483-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Decision Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a new two-parameter distribution over the bounded support (0,1) is introduced and studied in detail. Some of the interesting statistical properties like concavity, hazard rate function, mean residual life, moments and quantile function are discussed. The method of moments and maximum likelihood estimation methods are used to estimate unknown parameters of the proposed model. Besides, finite sample performance of estimation methods are evaluated through the Monte-Carlo simulation study. Application of the proposed distribution to the real data sets shows a better fit than many known two-parameter distributions on the unit interval. Moreover, a new regression model as an alternative to various unit interval regression models is introduced.
期刊介绍：
Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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
