{"title":"Uniformly more powerful tests for a subset of the components of a Normal Mean Vector","authors":"Yining Wang , Gang Li","doi":"10.1016/j.jspi.2023.106141","DOIUrl":null,"url":null,"abstract":"<div><p>A class of tests that are uniformly more powerful than the likelihood ratio test<span> is derived for testing the hypothesis about the means of a subset of the components of a multivariate normal distribution<span> with unknown covariance matrix, when the means of the other subset of the components are known.</span></span></p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"231 ","pages":"Article 106141"},"PeriodicalIF":0.8000,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Planning and Inference","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378375823001106","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
A class of tests that are uniformly more powerful than the likelihood ratio test is derived for testing the hypothesis about the means of a subset of the components of a multivariate normal distribution with unknown covariance matrix, when the means of the other subset of the components are known.
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The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists.
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