{"title":"事件时间数据的概率尺度残差","authors":"Eric S. Kawaguchi, Bryan E. Shepherd, Chun Li","doi":"arxiv-2409.11385","DOIUrl":null,"url":null,"abstract":"The probability-scale residual (PSR) is defined as $E\\{sign(y, Y^*)\\}$, where\n$y$ is the observed outcome and $Y^*$ is a random variable from the fitted\ndistribution. The PSR is particularly useful for ordinal and censored outcomes\nfor which fitted values are not available without additional assumptions.\nPrevious work has defined the PSR for continuous, binary, ordinal,\nright-censored, and current status outcomes; however, development of the PSR\nhas not yet been considered for data subject to general interval censoring. We\ndevelop extensions of the PSR, first to mixed-case interval-censored data, and\nthen to data subject to several types of common censoring schemes. We derive\nthe statistical properties of the PSR and show that our more general PSR\nencompasses several previously defined PSR for continuous and censored outcomes\nas special cases. The performance of the residual is illustrated in real data\nfrom the Caribbean, Central, and South American Network for HIV Epidemiology.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"18 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Probability-scale residuals for event-time data\",\"authors\":\"Eric S. Kawaguchi, Bryan E. Shepherd, Chun Li\",\"doi\":\"arxiv-2409.11385\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The probability-scale residual (PSR) is defined as $E\\\\{sign(y, Y^*)\\\\}$, where\\n$y$ is the observed outcome and $Y^*$ is a random variable from the fitted\\ndistribution. The PSR is particularly useful for ordinal and censored outcomes\\nfor which fitted values are not available without additional assumptions.\\nPrevious work has defined the PSR for continuous, binary, ordinal,\\nright-censored, and current status outcomes; however, development of the PSR\\nhas not yet been considered for data subject to general interval censoring. We\\ndevelop extensions of the PSR, first to mixed-case interval-censored data, and\\nthen to data subject to several types of common censoring schemes. We derive\\nthe statistical properties of the PSR and show that our more general PSR\\nencompasses several previously defined PSR for continuous and censored outcomes\\nas special cases. The performance of the residual is illustrated in real data\\nfrom the Caribbean, Central, and South American Network for HIV Epidemiology.\",\"PeriodicalId\":501425,\"journal\":{\"name\":\"arXiv - STAT - Methodology\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Methodology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.11385\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Methodology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11385","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
概率标度残差(PSR)定义为 $E\{sign(y,Y^*)\}$,其中$y$为观测结果,$Y^*$为拟合分布中的随机变量。以前的工作已经定义了连续、二元、序数、右删减和当前状态结果的 PSR;但是,对于一般区间删减的数据,尚未考虑开发 PSR。我们对 PSR 进行了扩展,首先适用于混合情况下的区间删失数据,然后适用于几种常见删失方案下的数据。我们推导出了 PSR 的统计特性,并表明我们更通用的 PSR 包含了之前定义的几种用于连续和剔除结果的 PSR 作为特例。来自加勒比、中美洲和南美洲艾滋病流行病学网络的真实数据说明了残差的性能。
The probability-scale residual (PSR) is defined as $E\{sign(y, Y^*)\}$, where
$y$ is the observed outcome and $Y^*$ is a random variable from the fitted
distribution. The PSR is particularly useful for ordinal and censored outcomes
for which fitted values are not available without additional assumptions.
Previous work has defined the PSR for continuous, binary, ordinal,
right-censored, and current status outcomes; however, development of the PSR
has not yet been considered for data subject to general interval censoring. We
develop extensions of the PSR, first to mixed-case interval-censored data, and
then to data subject to several types of common censoring schemes. We derive
the statistical properties of the PSR and show that our more general PSR
encompasses several previously defined PSR for continuous and censored outcomes
as special cases. The performance of the residual is illustrated in real data
from the Caribbean, Central, and South American Network for HIV Epidemiology.