{"title":"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","authors":"Mingyue Du, Mengzhu Yu","doi":"10.1080/10485252.2023.2280016","DOIUrl":null,"url":null,"abstract":"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).","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"47 3","pages":"0"},"PeriodicalIF":0.8000,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Regression analysis of multivariate interval-censored failure time data with a cured subgroup and informative censoring\",\"authors\":\"Mingyue Du, Mengzhu Yu\",\"doi\":\"10.1080/10485252.2023.2280016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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).\",\"PeriodicalId\":50112,\"journal\":{\"name\":\"Journal of Nonparametric Statistics\",\"volume\":\"47 3\",\"pages\":\"0\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2023-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Nonparametric Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/10485252.2023.2280016\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nonparametric Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10485252.2023.2280016","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
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).
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.