Jackknifed random weighting for Cox proportional hazards model  

作　　者：LI Xiao 1 ,WU YaoHua 2,& TU DongSheng 1 1 Cancer Research Institute,Queen’s University,Kingston,Ontario K 7L 3N6,Canada 2 Department of Finance and Statistics,University of Science and Technology of China,Hefei 230026,China 

出　　处：《Science China Mathematics》2012年第4期775-786,共12页中国科学：数学（英文版）

基　　金：supported by Natural Science and Engineering Research Council of Canada and National Natural Science Foundation of China (Grant No. 10871188)

摘　　要：The Cox proportional hazards model is the most used statistical model in the analysis of survival time data.Recently,a random weighting method was proposed to approximate the distribution of the maximum partial likelihood estimate for the regression coefficient in the Cox model.This method was shown not as sensitive to heavy censoring as the bootstrap method in simulation studies but it may not be second-order accurate as was shown for the bootstrap approximation.In this paper,we propose an alternative random weighting method based on one-step linear jackknife pseudo values and prove the second accuracy of the proposed method.Monte Carlo simulations are also performed to evaluate the proposed method for fixed sample sizes.The Cox proportional hazards model is the most used statistical model in the analysis of survival time data. Recently, a random weighting method was proposed to approximate the distribution of the maximum partial likelihood estimate for the regression coefficient in the Cox model. This method was shown not as sensitive to heavy censoring as the bootstrap method in simulation studies but it may not be second-order accurate as was shown for the bootstrap approximation. In this paper, we propose an alternative random weighting method based on one-step linear jackknife pseudo values and prove the second accuracy of the proposed method. Monte Carlo simulations are also performed to evaluate the proposed method for fixed sample sizes.

关 键 词：Cox proportional hazards model JACKKNIFE random weighting second-order accuracy simulations survival data 

分 类 号：O211.67[理学—概率论与数理统计]