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 Power-transformed linear quantile regression estimation for censored competing risks data Fan, Caiyun1; Zhang, Feipeng2; Zhou, Yong3,4 2017 Source Publication Statistics and Its Interface ISSN 1938-7989 Volume 10Issue:2Pages:239-254 Abstract This paper considers a power-transformed linear quantile regression model for censored competing risks data, based on conditional quantiles defined by using the cumulative incidence function. We propose a two-stage estimating procedure for the regression coefficients and the transformation parameter. In the first step, for a given transformation parameter, we develop an unbiased monotone estimating equation for regression parameters in the quantile model, which can be solved by minimizing a L-1 type convex objective function. In the second step, the transformation parameter can be estimated by constructing the cumulative sum processes. The consistency and asymptotic normality of the regression parameters and transformation parameter are derived. The finite-sample performances of the proposed approach are illustrated by simulation studies and an application to the follicular type lymphoma data set. Keyword Box-Cox transformation Censored data Competing risks Quantile regression Language 英语 Funding Project Shanghai Pujiang Program[16PJC041] ; Shanghai Young Teacher Training Scheme of Universities[ZZSWM15019] ; Shanghai Summit and Plateau Discipline ; National Natural Science Foundation of China (NSFC)[11401194] ; National Natural Science Foundation of China (NSFC)[71271128] ; Fundamental Research Funds for the Central Universities[531107050739] ; State Key Program of National Natural Science Foundation of China[71331006] ; State Key Program of National Natural Science Foundation of China[91546202] ; National Center for Mathematics and Interdisciplinary Sciences (NCMIS), Key Laboratory of RCSDS, Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences (CAS)[2008DP173182] ; Shanghai First-class Discipline A, Program for Changjiang Scholars (PCSIRT) and Innovative Research Team in Shanghai University of Finance and Economics (SUFE)[IRT13077] WOS Research Area Mathematical & Computational Biology ; Mathematics WOS Subject Mathematical & Computational Biology ; Mathematics, Interdisciplinary Applications WOS ID WOS:000389015500008 Publisher INT PRESS BOSTON, INC Citation statistics Document Type 期刊论文 Identifier http://ir.amss.ac.cn/handle/2S8OKBNM/24303 Collection 应用数学研究所 Corresponding Author Zhang, Feipeng Affiliation 1.Shanghai Univ Int Business & Econ, Sch Stat & Informat, Shanghai 201620, Peoples R China2.Hunan Univ, Sch Finance & Stat, Changsha 410082, Hunan, Peoples R China3.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China Recommended CitationGB/T 7714 Fan, Caiyun,Zhang, Feipeng,Zhou, Yong. Power-transformed linear quantile regression estimation for censored competing risks data[J]. Statistics and Its Interface,2017,10(2):239-254. APA Fan, Caiyun,Zhang, Feipeng,&Zhou, Yong.(2017).Power-transformed linear quantile regression estimation for censored competing risks data.Statistics and Its Interface,10(2),239-254. MLA Fan, Caiyun,et al."Power-transformed linear quantile regression estimation for censored competing risks data".Statistics and Its Interface 10.2(2017):239-254.
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