具有治愈子群和信息剔除的多变量间隔剔除失效时间数据的回归分析

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Journal of Nonparametric Statistics Pub Date : 2023-11-10 DOI:10.1080/10485252.2023.2280016
Mingyue Du, Mengzhu Yu
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引用次数: 0

摘要

当一个故障时间研究涉及几个相关的感兴趣的故障时间,并且每个故障时间只有间隔截短的观测值时,就会出现多变量间隔截短故障时间数据。尽管已经建立了大量的文献来进行回归分析,但似乎没有一种方法适用于既存在治愈子群又存在信息审查的情况,这是本文的重点。针对这一问题,提出了一类半参数变换非混合固化模型,并给出了两步估计方法。为了实现所提出的方法,开发了一种EM算法。数值结果表明,该方法能较好地满足实际情况,具有一定的应用价值。关键词:信息性审查多元间隔审查数据非混合治疗模型转换模型致谢感谢主编、副主编吕文斌教授和两位审稿人的宝贵意见和建议,使本文得到了极大的改进。实现所建议方法的R代码可根据要求从第二作者处获得。披露声明作者未报告潜在的利益冲突。
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Regression analysis of multivariate interval-censored failure time data with a cured subgroup and informative censoring
AbstractMultivariate interval-censored failure time data occur when a failure time study involves several related failure times of interest and only interval-censored observations are available for each of them. Although a great deal of literature has been established for their regression analysis, there does not seem to exist an approach that applies to the situation where there exist both a cured subgroup and informative censoring, the focus of this paper. For the problem, a class of semiparametric transformation non-mixture cure models is presented and a two-step estimation procedure is proposed. For the implementation of the proposed method, an EM algorithm is developed. Numerical results suggest that the proposed method works well for practical situations and an application is provided.Keywords: Informative censoringmultivariate interval-censored datanon-mixture cure modeltransformation model AcknowledgementsThe authors wish to thank the Editor, Prof. Wenbin Lu, the Associate Editor and two reviewers for their helpful comments and suggestions that greatly improved the paper. The R code for the implementation of the proposed method is available from the second author upon request.Disclosure statementNo potential conflict of interest was reported by the author(s).
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来源期刊
Journal of Nonparametric Statistics
Journal of Nonparametric Statistics 数学-统计学与概率论
CiteScore
1.50
自引率
8.30%
发文量
42
审稿时长
6-12 weeks
期刊介绍: Journal of Nonparametric Statistics provides a medium for the publication of research and survey work in nonparametric statistics and related areas. The scope includes, but is not limited to the following topics: Nonparametric modeling, Nonparametric function estimation, Rank and other robust and distribution-free procedures, Resampling methods, Lack-of-fit testing, Multivariate analysis, Inference with high-dimensional data, Dimension reduction and variable selection, Methods for errors in variables, missing, censored, and other incomplete data structures, Inference of stochastic processes, Sample surveys, Time series analysis, Longitudinal and functional data analysis, Nonparametric Bayes methods and decision procedures, Semiparametric models and procedures, Statistical methods for imaging and tomography, Statistical inverse problems, Financial statistics and econometrics, Bioinformatics and comparative genomics, Statistical algorithms and machine learning. Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order. Authors are encouraged to submit supplementary technical arguments, computer code, data analysed in the paper or any additional information for online publication along with the published paper.
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