{"title":"Testing powers of the ratio of variances of two normal populations with a common mean","authors":"Pravash Jena, Manas Ranjan Tripathy","doi":"10.1080/00949655.2023.2276306","DOIUrl":null,"url":null,"abstract":"AbstractThis article addresses the problem of hypothesis testing about the powers of the ratio of variances of two normal populations with a common mean. Different test procedures are proposed, such as the likelihood ratio test, the standardized likelihood ratio test, the parametric bootstrap likelihood ratio test, the computational approach test and its modification. Further, several generalized p-value approach test procedures are derived using some of the existing common mean estimators. The performances of all the suggested test methods are compared numerically in terms of their size values and power functions. In light of our simulation findings, we provide a few suggestions for utilizing the proposed test methods. Finally, we analyse real-life data to show the potential application of the proposed model.Keywords: Bootstrap samplescommon meangeneralized p-valueplug-in estimatorsratio of variancespower functionsimulation studysize value2010 AMS Subject Classifications: 62F0362F0562F1065C05 AcknowledgmentsThe authors would like to sincerely thank the two anonymous reviewers, whose constructive and thoughtful comments on the earlier version of the manuscript led to greater improvements in the manuscript's content.Disclosure statementThe authors declare that there are no relevant financial or non-financial competing interests to report for this work.","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"106 13","pages":"0"},"PeriodicalIF":1.1000,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Computation and Simulation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/00949655.2023.2276306","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
AbstractThis article addresses the problem of hypothesis testing about the powers of the ratio of variances of two normal populations with a common mean. Different test procedures are proposed, such as the likelihood ratio test, the standardized likelihood ratio test, the parametric bootstrap likelihood ratio test, the computational approach test and its modification. Further, several generalized p-value approach test procedures are derived using some of the existing common mean estimators. The performances of all the suggested test methods are compared numerically in terms of their size values and power functions. In light of our simulation findings, we provide a few suggestions for utilizing the proposed test methods. Finally, we analyse real-life data to show the potential application of the proposed model.Keywords: Bootstrap samplescommon meangeneralized p-valueplug-in estimatorsratio of variancespower functionsimulation studysize value2010 AMS Subject Classifications: 62F0362F0562F1065C05 AcknowledgmentsThe authors would like to sincerely thank the two anonymous reviewers, whose constructive and thoughtful comments on the earlier version of the manuscript led to greater improvements in the manuscript's content.Disclosure statementThe authors declare that there are no relevant financial or non-financial competing interests to report for this work.
期刊介绍:
Journal of Statistical Computation and Simulation ( JSCS ) publishes significant and original work in areas of statistics which are related to or dependent upon the computer.
Fields covered include computer algorithms related to probability or statistics, studies in statistical inference by means of simulation techniques, and implementation of interactive statistical systems.
JSCS does not consider applications of statistics to other fields, except as illustrations of the use of the original statistics presented.
Accepted papers should ideally appeal to a wide audience of statisticians and provoke real applications of theoretical constructions.