Smoothed empirical likelihood confidence intervals for the relative distribution with left-truncated and right-censored data

2010 ◽  
Vol 38 (3) ◽  
pp. 453-473 ◽  
Author(s):  
Elisa M. Molanes-lopez ◽  
Ricardo Cao ◽  
Ingrid VAN Keilegom
Keyword(s):  
Confidence Intervals ◽  
Censored Data ◽  
Empirical Likelihood ◽  
Relative Distribution ◽  
Right Censored Data ◽  
Smoothed Empirical Likelihood

Novel empirical likelihood inference for the mean difference with right-censored data

2021 ◽  
pp. 096228022110417
Author(s):  
Kangni Alemdjrodo ◽  
Yichuan Zhao
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Censored Data ◽  
Empirical Likelihood ◽  
Likelihood Method ◽  
Right Censored Data ◽  
Empirical Likelihood Method ◽  
Chi Squared ◽  
Coverage Accuracy ◽  
The Mean

This paper focuses on comparing two means and finding a confidence interval for the difference of two means with right-censored data using the empirical likelihood method combined with the independent and identically distributed random functions representation. In the literature, some early researchers proposed empirical link-based confidence intervals for the mean difference based on right-censored data using the synthetic data approach. However, their empirical log-likelihood ratio statistic has a scaled chi-squared distribution. To avoid the estimation of the scale parameter in constructing confidence intervals, we propose an empirical likelihood method based on the independent and identically distributed representation of Kaplan–Meier weights involved in the empirical likelihood ratio. We obtain the standard chi-squared distribution. We also apply the adjusted empirical likelihood to improve coverage accuracy for small samples. In addition, we investigate a new empirical likelihood method, the mean empirical likelihood, within the framework of our study. The performances of all the empirical likelihood methods are compared via extensive simulations. The proposed empirical likelihood-based confidence interval has better coverage accuracy than those from existing methods. Finally, our findings are illustrated with a real data set.

Influence function-based empirical likelihood for inference of quantile medical costs with censored data

2019 ◽  
Vol 29 (7) ◽  
pp. 1913-1934
Author(s):  
Jenny Jeyarajah ◽  
Guanhao Wei ◽  
Gengsheng Qin
Keyword(s):  
Confidence Intervals ◽  
Censored Data ◽  
Empirical Likelihood ◽  
Influence Function ◽  
Likelihood Ratio Statistic ◽  
Medical Costs ◽  
Finite Sample ◽  
Chi Square ◽  
Coverage Probabilities ◽  
Interval Lengths

In this paper, we propose empirical likelihood methods based on influence function and Jackknife techniques to construct confidence intervals for quantile medical costs with censored data. We show that the influence function-based empirical log-likelihood ratio statistic for the quantile medical cost has a standard Chi-square distribution as its asymptotic distribution. Simulation studies are conducted to compare coverage probabilities and interval lengths of the proposed empirical likelihood confidence intervals with the existing normal approximation-based confidence intervals for quantile medical costs. The proposed methods are observed to have better finite-sample performances than existing methods. The new methods are also illustrated through a real example.

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