右删失长度偏差数据分位数差的非参数估计

We propose a novel nonparametric estimator of the quantile difference based on the length-biased data subject to potential right censoring. In order to improve efficiency, the new estimator incorporates the auxiliary information inherent in the prevalent sampling design. And it has a simple expression, which is easy to compute. Moreover, the consistency and asymptotic normality of this quantile difference estimator are established using the empirical process theory and the asymptotic variance can be obtained consistently via a resampling method. We also demonstrate that the proposed estimator exhibits satisfactory performance with finite sample size through the Monte-Carlo studies and an analysis of a real data example about the Alzheimer's disease.