This paper considers a competing risks model for right-censored and length-biased survival data from prevalent sampling.We propose a nonparametric quantile inference procedure for cause-specific residual life distribu...This paper considers a competing risks model for right-censored and length-biased survival data from prevalent sampling.We propose a nonparametric quantile inference procedure for cause-specific residual life distribution with competing risks data.We also derive the asymptotic properties of the proposed estimators of this quantile function.Simulation studies and the unemployment data demonstrate the practical utility of the methodology.展开更多
We analyze left-truncated and right-censored data using Cox proportional hazard models with long-term survivors. The estimators of covariate coefficients and the long-term survivor proportion are obtained by the parti...We analyze left-truncated and right-censored data using Cox proportional hazard models with long-term survivors. The estimators of covariate coefficients and the long-term survivor proportion are obtained by the partial likelihood method, and their asymptotic properties are also established. Simulation studies demonstrate the performance of the proposed estimators, and an application to a real dataset is provided.展开更多
基金This paper is supported in part by the National Natural Science Foundation of China(Nos.11771133,11801360,91546202,71931004).
文摘This paper considers a competing risks model for right-censored and length-biased survival data from prevalent sampling.We propose a nonparametric quantile inference procedure for cause-specific residual life distribution with competing risks data.We also derive the asymptotic properties of the proposed estimators of this quantile function.Simulation studies and the unemployment data demonstrate the practical utility of the methodology.
基金Natural Science Funds for Distinguished Young Scholar (No. 70825004)Creative Research Groups of China (No. 10721101)+2 种基金Shanghai University of Finance and Economics Project 211 Phase ⅢShanghai Leading Academic Discipline Project (No. B803)Zhou's work was supported by Graduate Creation Funds of Shanghai University of Finance and Economics(No. CXJJ-2011-436)
文摘We analyze left-truncated and right-censored data using Cox proportional hazard models with long-term survivors. The estimators of covariate coefficients and the long-term survivor proportion are obtained by the partial likelihood method, and their asymptotic properties are also established. Simulation studies demonstrate the performance of the proposed estimators, and an application to a real dataset is provided.
