{"title":"随机删失数据的非参数检验","authors":"Ayushee, Narinder Kumar, Manish Goyal","doi":"10.1007/s40745-023-00500-5","DOIUrl":null,"url":null,"abstract":"<div><p>A nonparametric test for the testing of scale parameters, is proposed in two-sample situation with random censored data. Random censored data are mostly encountered in clinical studies, where some individuals experience the event of interest (death); some are drop-outs or loss to follow-ups and some are still alive at the end of study. The performance of test is studied by comparing it with some existing tests in terms of asymptotic relative efficiency. Critical values required for the test are computed. Statistical power of the test is assessed through simulation study with varying sample sizes and varying censoring percentages. The working of test is illustrated by applying it to a real-life data set.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Nonparametric Test for Randomly Censored Data\",\"authors\":\"Ayushee, Narinder Kumar, Manish Goyal\",\"doi\":\"10.1007/s40745-023-00500-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A nonparametric test for the testing of scale parameters, is proposed in two-sample situation with random censored data. Random censored data are mostly encountered in clinical studies, where some individuals experience the event of interest (death); some are drop-outs or loss to follow-ups and some are still alive at the end of study. The performance of test is studied by comparing it with some existing tests in terms of asymptotic relative efficiency. Critical values required for the test are computed. Statistical power of the test is assessed through simulation study with varying sample sizes and varying censoring percentages. The working of test is illustrated by applying it to a real-life data set.</p></div>\",\"PeriodicalId\":36280,\"journal\":{\"name\":\"Annals of Data Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-26\",\"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/s40745-023-00500-5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Decision Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Data Science","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s40745-023-00500-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Decision Sciences","Score":null,"Total":0}
A nonparametric test for the testing of scale parameters, is proposed in two-sample situation with random censored data. Random censored data are mostly encountered in clinical studies, where some individuals experience the event of interest (death); some are drop-outs or loss to follow-ups and some are still alive at the end of study. The performance of test is studied by comparing it with some existing tests in terms of asymptotic relative efficiency. Critical values required for the test are computed. Statistical power of the test is assessed through simulation study with varying sample sizes and varying censoring percentages. The working of test is illustrated by applying it to a real-life data set.
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.
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