{"title":"Maximum likelihood estimation of the parameters of the inverse Gaussian distribution using maximum rank set sampling with unequal samples","authors":"Shuo Wang, Wangxue Chen, Meng Chen, Ya Zhou","doi":"10.1080/08898480.2021.1996822","DOIUrl":null,"url":null,"abstract":"ABSTRACT Maximum ranked set sampling with unequal samples is a sampling procedure used to reduce the error of ranking of observations and increase the efficiency of statistical inference. It is used for maximum likelihood estimation of the location and shape parameters of the inverse Gaussian distribution. Its asymptotic efficiency is at least 1.4 times higher than those of estimators based on simple random sampling. It is useful in reliability studies and in Bayesian statistics involving the inverse Gaussian distribution.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"30 1","pages":"1 - 21"},"PeriodicalIF":1.4000,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Population Studies","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1080/08898480.2021.1996822","RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"DEMOGRAPHY","Score":null,"Total":0}
引用次数: 7
Abstract
ABSTRACT Maximum ranked set sampling with unequal samples is a sampling procedure used to reduce the error of ranking of observations and increase the efficiency of statistical inference. It is used for maximum likelihood estimation of the location and shape parameters of the inverse Gaussian distribution. Its asymptotic efficiency is at least 1.4 times higher than those of estimators based on simple random sampling. It is useful in reliability studies and in Bayesian statistics involving the inverse Gaussian distribution.
期刊介绍：
Mathematical Population Studies publishes carefully selected research papers in the mathematical and statistical study of populations. The journal is strongly interdisciplinary and invites contributions by mathematicians, demographers, (bio)statisticians, sociologists, economists, biologists, epidemiologists, actuaries, geographers, and others who are interested in the mathematical formulation of population-related questions.
The scope covers both theoretical and empirical work. Manuscripts should be sent to Manuscript central for review. The editor-in-chief has final say on the suitability for publication.
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