scholarly journals A Jackknife empirical likelihood approach for K ‐sample Tests

2021 ◽  
Author(s):  
Yongli Sang ◽  
Xin Dang ◽  
Yichuan Zhao
Keyword(s):  
Empirical Likelihood ◽  
Likelihood Approach ◽  
Jackknife Empirical Likelihood

scholarly journals Modelling complex survey data with population level information: an empirical likelihood approach

Biometrika ◽  
2016 ◽  
Vol 103 (2) ◽  
pp. 447-459 ◽  
Author(s):  
M. Oguz-Alper ◽  
Y. G. Berger
Keyword(s):  
Survey Data ◽  
Empirical Likelihood ◽  
Population Level ◽  
Complex Survey ◽  
Complex Survey Data ◽  
Level Information ◽  
Likelihood Approach

scholarly journals Jackknife empirical likelihood for growing dimensional nonsmooth estimating equations

2019 ◽  
Vol 49 (8) ◽  
pp. 1103
Author(s):  
Li Zhouping ◽  
Yang Fan ◽  
Wei Yang
Keyword(s):  
Empirical Likelihood ◽  
Estimating Equations ◽  
Jackknife Empirical Likelihood

scholarly journals Jackknife empirical likelihood methods for Gini correlations and their equality testing

2019 ◽  
Vol 199 ◽  
pp. 45-59 ◽  
Author(s):  
Yongli Sang ◽  
Xin Dang ◽  
Yichuan Zhao
Keyword(s):  
Empirical Likelihood ◽  
Likelihood Methods ◽  
Jackknife Empirical Likelihood

Bayesian jackknife empirical likelihood

Biometrika ◽  
2019 ◽  
Vol 106 (4) ◽  
pp. 981-988
Author(s):  
Y Cheng ◽  
Y Zhao
Keyword(s):  
Bayesian Inference ◽  
Empirical Likelihood ◽  
Small Sample ◽  
New Approach ◽  
U Statistics ◽  
Posterior Inference ◽  
Jackknife Empirical Likelihood ◽  
General Tool ◽  
Control Study ◽  
Better Than

Summary Empirical likelihood is a very powerful nonparametric tool that does not require any distributional assumptions. Lazar (2003) showed that in Bayesian inference, if one replaces the usual likelihood with the empirical likelihood, then posterior inference is still valid when the functional of interest is a smooth function of the posterior mean. However, it is not clear whether similar conclusions can be obtained for parameters defined in terms of $U$-statistics. We propose the so-called Bayesian jackknife empirical likelihood, which replaces the likelihood component with the jackknife empirical likelihood. We show, both theoretically and empirically, the validity of the proposed method as a general tool for Bayesian inference. Empirical analysis shows that the small-sample performance of the proposed method is better than its frequentist counterpart. Analysis of a case-control study for pancreatic cancer is used to illustrate the new approach.

scholarly journals Jackknife empirical likelihood-based inference for S-Gini indices

2019 ◽  
pp. 1-17
Author(s):  
N. Sreelakshmi ◽  
Sudheesh K. Kattumannil ◽  
Rituparna Sen
Keyword(s):  
Empirical Likelihood ◽  
Jackknife Empirical Likelihood

scholarly journals Depth-based weighted jackknife empirical likelihood for non-smooth U-structure equations

Test ◽  
2019 ◽  
Vol 29 (2) ◽  
pp. 573-598
Author(s):  
Yongli Sang ◽  
Xin Dang ◽  
Yichuan Zhao
Keyword(s):  
Empirical Likelihood ◽  
Structure Equations ◽  
Jackknife Empirical Likelihood
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