An empirical likelihood method in a partially linear single-index model with right censored data

2011 ◽  
Vol 28 (5) ◽  
pp. 1041-1060 ◽  
Author(s):  
Yi Ping Yang ◽  
Liu Gen Xue ◽  
Wei Hu Cheng
Keyword(s):  
Censored Data ◽  
Empirical Likelihood ◽  
Likelihood Method ◽  
Right Censored Data ◽  
Empirical Likelihood Method ◽  
Single Index ◽  
Index Model ◽  
Partially Linear ◽  
Single Index Model

scholarly journals Empirical Likelihood for Partially Linear Single-Index Models under Negatively Associated Errors

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Qi ◽  
ZhuoXi Yu
Keyword(s):  
Empirical Likelihood ◽  
Likelihood Ratio Statistic ◽  
Confidence Regions ◽  
Likelihood Method ◽  
Negatively Associated ◽  
Single Index ◽  
Index Model ◽  
Partially Linear ◽  
Blockwise Empirical Likelihood ◽  
Parameters Of Interest

In this paper, the authors consider the application of the blockwise empirical likelihood method to the partially linear single-index model when the errors are negatively associated, which often exist in sequentially collected economic data. Thereafter, the blockwise empirical likelihood ratio statistic for the parameters of interest is proved to be asymptotically chi-squared. Hence, it can be directly used to construct confidence regions for the parameters of interest. A few simulation experiments are used to illustrate our proposed method.

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.

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