Jackknife empirical likelihood-based inference for S-Gini indices

Sreelakshmi, N.; Kattumannil, Sudheesh K.; Sen, Rituparna

doi:10.1080/03610918.2019.1586930

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Here we compare the bootstrap-t confidence interval (Boot_t), better bootstrap confidence interval (Bca), and bootstrapcalibrated jackknife empirical likelihood confidence interval(BCJEL), and the specific construction are referred to [16] and [19]. Each experiment is repeated 1000 times with sizes n = 50, 75, 100, 150, and 200.…”

Section: Simulation Studymentioning

confidence: 99%

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Jackknife empirical likelihood for the Gini index with right censored observations

Xie

2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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Estimators of the Gini index have been proposed and widely utilized for interval estimation and hypothesis testing in economics. In this paper, Jackknife Empirical Likelihood (JEL) is developed for the Gini index with right censored observations. We prove the limiting distribution of JEL ratio statistics and construct the confidence intervals for the Gini index. Comparative simulation studies with existing methods demonstrate that our methods do better in coverage probability and average length.

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Section: Simulation Studymentioning

confidence: 99%

“…Wang obtained confidence intervals for the Gini index based on JEL [19] and also in 2016 they used JEL to discuss the differences between the Gini index under paired data and independent data respectively [18] . An extended version of the Gini index, the single-series Gini index is estimated by EL and JEL in 2019 [16] .…”

Section: Introductionmentioning

confidence: 99%

Jackknife empirical likelihood for the Gini index with right censored observations

Xie

2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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“…Sang et al (2019) developed the JEL method for Gini correlations. Sreelakshmi et al (2021) studied the JEL based inference for the S‐Gini indices. Alemdjrodo and Zhao (2019) reduced the computation in jackknife empirical likelihood for comparing two correlated Gini indices.…”

Section: Variants Of Empirical Likelihoodmentioning

confidence: 99%

A review of recent advances in empirical likelihood

Liu

Zhao

2022

WIREs Computational Stats

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Empirical likelihood is widely used in many statistical problems. In this article, we provide a review of the empirical likelihood method, due to its significant development in recent years. Since the introduction of empirical likelihood, variants of empirical likelihood have been proposed, and the applications of empirical likelihood in high dimensions have also been studied. It is necessary to summarize the new development of empirical likelihood. In this article, we give a review of the Bayesian empirical likelihood, the bias-corrected empirical likelihood, the jackknife empirical likelihood, the adjusted empirical likelihood, the extended empirical likelihood, the transformed empirical likelihood, the mean empirical likelihood, and the empirical likelihood with high dimensions. Finally, we have a brief survey of the computation and implementation for empirical likelihood methods.

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A robust estimator of the S-Gini index for massive data

Mohamed

Rassoul

2021

Communications in Statistics - Simulation and Computation

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Jackknife empirical likelihood-based inference for S-Gini indices

Cited by 4 publications

References 24 publications

Jackknife empirical likelihood for the Gini index with right censored observations

Jackknife empirical likelihood for the Gini index with right censored observations

A review of recent advances in empirical likelihood

A robust estimator of the S-Gini index for massive data

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