Estimating equation estimators of quantile differences for one sample with length-biased and right-censored data

Abstract

This paper estimates quantile differences for one sample with length-biased and right-censored (LBRC) data. To ensure the asymptotic unbiasedness of the estimator, the estimating equation method is adopted. To improve the efficiency of the estimator, in the sense of having a lower mean squared error, the kernel-smoothed approach is employed. To make full use of the features of LBRC data, the augmented inverse probability complete case weight is investigated in detail. Moreover, the consistency and asymptotic normality of the proposed estimators are established. The numerical simulations are conducted to examine the performance of the estimators.