{"title":"Kaplan-Meier积极限估计量的概率质量函数","authors":"Yuxin Qin, H. Sasinowska, L. Leemis","doi":"10.1080/00031305.2022.2070279","DOIUrl":null,"url":null,"abstract":"Abstract Kaplan and Meier’s 1958 article developed a nonparametric estimator of the survivor function from a right-censored dataset. Determining the size of the support of the estimator as a function of the sample size provides a challenging exercise for students in an advanced course in mathematical statistics. We devise two algorithms for calculating the support size and calculate the associated probability mass function for small sample sizes and particular probability distributions for the failure and censoring times.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Probability Mass Function of the Kaplan–Meier Product–Limit Estimator\",\"authors\":\"Yuxin Qin, H. Sasinowska, L. Leemis\",\"doi\":\"10.1080/00031305.2022.2070279\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Kaplan and Meier’s 1958 article developed a nonparametric estimator of the survivor function from a right-censored dataset. Determining the size of the support of the estimator as a function of the sample size provides a challenging exercise for students in an advanced course in mathematical statistics. We devise two algorithms for calculating the support size and calculate the associated probability mass function for small sample sizes and particular probability distributions for the failure and censoring times.\",\"PeriodicalId\":342642,\"journal\":{\"name\":\"The American Statistician\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The American Statistician\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/00031305.2022.2070279\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The American Statistician","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/00031305.2022.2070279","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Probability Mass Function of the Kaplan–Meier Product–Limit Estimator
Abstract Kaplan and Meier’s 1958 article developed a nonparametric estimator of the survivor function from a right-censored dataset. Determining the size of the support of the estimator as a function of the sample size provides a challenging exercise for students in an advanced course in mathematical statistics. We devise two algorithms for calculating the support size and calculate the associated probability mass function for small sample sizes and particular probability distributions for the failure and censoring times.