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Non-crossing weighted kernel quantile regression with right censored data

Authors
Bang, SungwanEo, Soo-HeangCho, Yong MeeJhun, MyoungshicCho, HyungJun
Issue Date
1월-2016
Publisher
SPRINGER
Keywords
Kernel; Multiple quantiles regression; Non-crossing; Right censored data
Citation
LIFETIME DATA ANALYSIS, v.22, no.1, pp.100 - 121
Indexed
SCIE
SCOPUS
Journal Title
LIFETIME DATA ANALYSIS
Volume
22
Number
1
Start Page
100
End Page
121
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/89918
DOI
10.1007/s10985-014-9314-8
ISSN
1380-7870
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.
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정경대학 (통계학과)
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