Quantile difference estimation with censoring indicators missing at random

Speaker: Hanying Liang (Tongji University)

Date: November 25 Thur. 4:00pm

Venue: Room200-9, Sir Shaw Run Run Business Administration building, School of Mathematical Sciences, Yuquan Campus

Abstract: In this talk, we propose estimator of distribution function when the data are right-censored and the censoring indicators are missing at random, and then establish their strong representations and asymptotic normality. Further, based on empirical likelihood method, we define maximum empirical likelihood estimators and smoothed log-likelihood ratios of two-sample quantile difference in the presence and absence of auxiliary information, respectively, and prove their asymptotic distributions. Simulation study and real data analysis are conducted to investigate the finite sample behavior of the proposed methods.