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Quantile difference estimation with censoring indicators missing at random

2021-11-22

Speaker: LIANG Hanying, Professor, School of Mathematical Science, Tongji University

Venue: Room 200-9, Run Run Shaw Business Administration Building, 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.