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Nonparametric and semiparametric estimation of quantile residual lifetime for length-biased and right-censored data
Wang, Yixin1,2; Zhou, Zhefang3; Zhou, Xiao-Hua4,5; Zhou, Yong1,2
2017-06-01
Source PublicationCANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
ISSN0319-5724
Volume45Issue:2Pages:220-250
AbstractQuantile residual lifetime models are often of concern in survival analysis, especially when studying a chronic or irreversible disease like dementia. In the past several decades residual life models have been studied extensively with right-censored survival data. However these methods are not suitable to analyze the length-biased and right-censored data from the prevalent cohort sampling. In this article we propose nonparametric and semiparametric model-based procedures to estimate the quantile residual lifetime with censored length-biased data. Two test statistics are established for comparing the quantile residual lifetimes of two groups, evaluated, respectively, on ratio and difference in terms of type I error probabilities and powers. Some simulations are conducted to compare the proposed method with existing approaches. Real dementia data from the National Alzheimer's Coordinating Center are used to illustrate the proposed estimation methods by estimating the quantile residual lifetimes of the dementia patients. The Canadian Journal of Statistics 45: 220-250; 2017 (c) 2017 Statistical Society of Canada
KeywordCox model length-bias quantile residual lifetime model right-censoring
DOI10.1002/cjs.11319
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[71271128] ; NSFC[71331006] ; NSFC[91546202] ; National Center for Mathematics and Interdisciplinary Sciences (NCMIS) ; Key Laboratory of RCSDS, AMSS, CAS[2008DP173182] ; Innovative Research Team of Shanghai University of Finance and Economics[IRTSHUFE13122402] ; Program for Changjiang Scholars Innovative Research Team of Ministry of Education[IRT13077] ; Natural Science Foundation of Guang Dong[2015A030313833] ; Shanghai University of Finance and Economics ; China Postdoctoral Science Foundation[2015M581186]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000400027400006
PublisherWILEY
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/25986
Collection应用数学研究所
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
2.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China
3.Beijing Normal Univ Hong Kong Baptist Univ, United Int Coll, Zhuhai 519085, Guangdong, Peoples R China
4.Univ Washington, Dept Biostat, Seattle, WA 98195 USA
5.US Dept Vet Affairs, Biostat Unit, Seattle Med Ctr, Seattle, WA 98104 USA
Recommended Citation
GB/T 7714
Wang, Yixin,Zhou, Zhefang,Zhou, Xiao-Hua,et al. Nonparametric and semiparametric estimation of quantile residual lifetime for length-biased and right-censored data[J]. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE,2017,45(2):220-250.
APA Wang, Yixin,Zhou, Zhefang,Zhou, Xiao-Hua,&Zhou, Yong.(2017).Nonparametric and semiparametric estimation of quantile residual lifetime for length-biased and right-censored data.CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE,45(2),220-250.
MLA Wang, Yixin,et al."Nonparametric and semiparametric estimation of quantile residual lifetime for length-biased and right-censored data".CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 45.2(2017):220-250.
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