{"title":"Kendall的置信区间","authors":"J. Long, N. Cliff","doi":"10.1111/J.2044-8317.1997.TB01100.X","DOIUrl":null,"url":null,"abstract":"A simulation study was conducted to examine the performance of several confidence intervals (CIs) for Kendall's tau (txy) under a variety of population conditions. Two normal population variables (N = 10,000) were transformed to have tau correlations, τ = 0, .19, .41, or.71. Samples (n = 10, 50, 200) were drawn from the transformed populations 2000 times under each level of correlation, and accompanying CIs were computed on each sample. The results show that the CI for τ based on a consistent estimate of the variance of txy has the best coverage and power among a number of alternatives. Kendall's txy is unaffected by non-normality induced by monotonic transformations and, with its consistent variance estimated from the sample, performs well under a wide range of conditions.","PeriodicalId":229922,"journal":{"name":"British Journal of Mathematical and Statistical Psychology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"42","resultStr":"{\"title\":\"Confidence intervals for Kendall's tau\",\"authors\":\"J. Long, N. Cliff\",\"doi\":\"10.1111/J.2044-8317.1997.TB01100.X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A simulation study was conducted to examine the performance of several confidence intervals (CIs) for Kendall's tau (txy) under a variety of population conditions. Two normal population variables (N = 10,000) were transformed to have tau correlations, τ = 0, .19, .41, or.71. Samples (n = 10, 50, 200) were drawn from the transformed populations 2000 times under each level of correlation, and accompanying CIs were computed on each sample. The results show that the CI for τ based on a consistent estimate of the variance of txy has the best coverage and power among a number of alternatives. Kendall's txy is unaffected by non-normality induced by monotonic transformations and, with its consistent variance estimated from the sample, performs well under a wide range of conditions.\",\"PeriodicalId\":229922,\"journal\":{\"name\":\"British Journal of Mathematical and Statistical Psychology\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"42\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"British Journal of Mathematical and Statistical Psychology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/J.2044-8317.1997.TB01100.X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Mathematical and Statistical Psychology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/J.2044-8317.1997.TB01100.X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A simulation study was conducted to examine the performance of several confidence intervals (CIs) for Kendall's tau (txy) under a variety of population conditions. Two normal population variables (N = 10,000) were transformed to have tau correlations, τ = 0, .19, .41, or.71. Samples (n = 10, 50, 200) were drawn from the transformed populations 2000 times under each level of correlation, and accompanying CIs were computed on each sample. The results show that the CI for τ based on a consistent estimate of the variance of txy has the best coverage and power among a number of alternatives. Kendall's txy is unaffected by non-normality induced by monotonic transformations and, with its consistent variance estimated from the sample, performs well under a wide range of conditions.