{"title":"Generalized Quasi Lindley Distribution: Theoretical Properties, Estimation Methods, and Applications","authors":"A. Al-Omari, SidAhmed Benchiha","doi":"10.1285/I20705948V14N1P167","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce a new continuous distribution of two parameterscalled as a generalized Quasi Lindley distribution (GQLD). The GQLD is asum of two independent Quasi Lindley distributed random variables. Compre-hensive statistical properties of the GQLD are provided in closed forms includesmoments, reliability analysis, stochastic ordering, stress-strength reliability, andthe distribution of order statistics. The parameters of the new distribution areestimated by the maximum likelihood, maximum product of spacings, ordinaryleast squares, weighted least squares, Cramer-von-Mises, and Anderson-Darlingmethods are considered. A simulation study is conducted to investigate theeciency of the proposed estimators and applications to real data sets are pro-vided.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"14 1","pages":"167-196"},"PeriodicalIF":0.6000,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Journal of Applied Statistical Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1285/I20705948V14N1P167","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: 4
Abstract
In this paper, we introduce a new continuous distribution of two parameterscalled as a generalized Quasi Lindley distribution (GQLD). The GQLD is asum of two independent Quasi Lindley distributed random variables. Compre-hensive statistical properties of the GQLD are provided in closed forms includesmoments, reliability analysis, stochastic ordering, stress-strength reliability, andthe distribution of order statistics. The parameters of the new distribution areestimated by the maximum likelihood, maximum product of spacings, ordinaryleast squares, weighted least squares, Cramer-von-Mises, and Anderson-Darlingmethods are considered. A simulation study is conducted to investigate theeciency of the proposed estimators and applications to real data sets are pro-vided.
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