Quantile difference estimation with censoring indicators missing at random

Cui-Juan Kong () and Han-Ying Liang ()
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Cui-Juan Kong: Shandong University
Han-Ying Liang: Tongji University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2024, vol. 30, issue 2, No 4, 345-382

Abstract: Abstract In this paper, we define estimators of distribution functions when the data are right-censored and the censoring indicators are missing at random, and establish their strong representations and asymptotic normality. Besides, based on empirical likelihood method, we define maximum empirical likelihood estimators and smoothed log-empirical 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.

Keywords: Asymptotic distribution; Distribution function estimation; Missing at random; Quantile difference; Right-censored; 62N02; 62F12 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10985-023-09614-7

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