title: Non-crossing weighted kernel quantile regression with right censored data.
abstract: Regarding survival data analysis in regression modeling, multiple conditional quantiles are useful summary statistics to assess covariate effects on survival times. In this study, we consider an estimation problem of multiple nonlinear quantile functions with right censored survival data. To account for censoring in estimating a nonlinear quantile function, weighted kernel quantile regression (WKQR) has been developed by using the kernel trick and inverse-censoring-probability weights. However, the individually estimated quantile functions based on the WKQR often cross each other and consequently violate the basic properties of quantiles. To avoid this problem of quantile crossing, we propose the non-crossing weighted kernel quantile regression (NWKQR), which estimates multiple nonlinear conditional quantile functions simultaneously by enforcing the non-crossing constraints on kernel coefficients. The numerical results are presented to demonstrate the competitive performance of the proposed NWKQR over the WKQR.
authors.affiliation: Department of Mathematics, Korea Military Academy, P.O. Box 77, Seoul, Republic of Korea.
authors.forename: Sungwan
authors.initials: S
authors.lastname: Bang
authors.affiliation: Department of Statistics, Korea University, Seoul, 136-701, Republic of Korea.
authors.forename: Soo-Heang
authors.initials: SH
authors.lastname: Eo
authors.affiliation: Department of Pathology, Asan Medical Center, Seoul, 138-736, Republic of Korea.
authors.forename: Yong Mee
authors.initials: YM
authors.lastname: Cho
authors.affiliation: Department of Statistics, Korea University, Seoul, 136-701, Republic of Korea.
authors.forename: Myoungshic
authors.initials: M
authors.lastname: Jhun
authors.affiliation: Department of Statistics, Korea University, Seoul, 136-701, Republic of Korea. hj4cho@korea.ac.kr.
authors.forename: HyungJun
authors.initials: H
authors.lastname: Cho
mesh_terms: D000465:Algorithms; D002292:Carcinoma, Renal Cell / Q000401:mortality / Q000473:pathology; D003198:Computer Simulation; D016027:Heart Transplantation / Q000401:mortality; D006801:Humans; D007680:Kidney Neoplasms / Q000401:mortality / Q000473:pathology; D015233:Models, Statistical; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D016019:Survival Analysis
keywords: Kernel; Multiple quantiles regression; Non-crossing; Right censored data