Lifetime Data Anal - Non-crossing weighted kernel quantile regression with right censored data.

Tópicos

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Resumo

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.

Resumo Limpo

regard surviv data analysi regress model multipl condit quantil use summari statist assess covari effect surviv time studi consid estim problem multipl nonlinear quantil function right censor surviv data account censor estim nonlinear quantil function weight kernel quantil regress wkqr develop use kernel trick inversecensoringprob weight howev individu estim quantil function base wkqr often cross consequ violat basic properti quantil avoid problem quantil cross propos noncross weight kernel quantil regress nwkqr estim multipl nonlinear condit quantil function simultan enforc noncross constraint kernel coeffici numer result present demonstr competit perform propos nwkqr wkqr

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