Probability-scale residuals for event-time data

Eric S. Kawaguchi, Bryan E. Shepherd, Chun Li
{"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}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
事件时间数据的概率尺度残差
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Poisson approximate likelihood compared to the particle filter Optimising the Trade-Off Between Type I and Type II Errors: A Review and Extensions Bias Reduction in Matched Observational Studies with Continuous Treatments: Calipered Non-Bipartite Matching and Bias-Corrected Estimation and Inference Forecasting age distribution of life-table death counts via α-transformation Probability-scale residuals for event-time data
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1