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 |
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| container_title | Lifetime data analysis |
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| creator | Kong, Cui-Juan Liang, Han-Ying |
| description | 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. |
| doi_str_mv | 10.1007/s10985-023-09614-7 |
| format | Article |
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| subjects | Asymptotic methods Asymptotic properties Computer Simulation Data Analysis Data Interpretation, Statistical Distribution functions Economics Empirical analysis Estimators Finance Health Sciences Humans Indicators Insurance Likelihood ratio Management Mathematics and Statistics Medicine Normality Operations Research/Decision Theory Probability Quality Control Quantiles Random variables Reliability Safety and Risk Statistics Statistics for Business Statistics for Life Sciences Survival analysis |
| title | Quantile difference estimation with censoring indicators missing at random |
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