Non-crossing weighted kernel quantile regression with right censored data

Sungwan Bang, Soo-Heang Eo, Yong Mee Cho, Myoungshic Jhun and HyungJun Cho ()
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Sungwan Bang: Korea Military Academy
Soo-Heang Eo: Korea University
Yong Mee Cho: Asan Medical Center
Myoungshic Jhun: Korea University
HyungJun Cho: Korea University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2016, vol. 22, issue 1, No 5, 100-121

Abstract: 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.

Keywords: Kernel; Multiple quantiles regression; Non-crossing; Right censored data (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10985-014-9314-8

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