Abstract
Smoothed Jackknife Empirical Likelihood for Weighted Rank Regression with Censored Data
Highlights
A primary interest of survival analysis is often to understand the relationship between survival times and covariates measured on study participants, such as physical and biological measurements and medical conditions
We have developed the smoothed jackknife empirical likelihood (JEL) method, a new inference method for the regression parameters in the accelerated failure time (AFT) model with right censored data containing outlying response or covariate values
Based on the weighted smoothed rank estimation function proposed by Heller [2], jackknife and empirical likelihood are integrated to yield the new method
Summary
A primary interest of survival analysis is often to understand the relationship between survival times and covariates measured on study participants, such as physical and biological measurements and medical conditions. Survival data are not fully observed on all subjects, but rather some response values are censored. For i = 1, , n, let Ti represent the survival time for the ith subject, Xi be the associated p-dimensional vector of covariates, Ci denote the censoring time and δi denote the event indicator, i.e.,= δi I (Ti ≤ Ci ), which takes the value 1 if the event time is observed, or 0 if the event time is censored. Conditional on the covariates for the ith subject, Ci is assumed to be independent of the failure times Ti. We define Yi as the minimum of the survival time and the censoring time, i.e., Yi = min (Ti,Ci ).
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