Quantile difference estimation with censoring indicators missing at random

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 likelihoo...

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Veröffentlicht in:Lifetime data analysis 2024-04, Vol.30 (2), p.345-382
Hauptverfasser: Kong, Cui-Juan, Liang, Han-Ying
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:1380-7870
1572-9249
1572-9249
DOI:10.1007/s10985-023-09614-7