<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>We propose an estimator for the Gini coefficient, based on a ratio of means. We show how bootstrap and empirical likelihood can be combined to construct confidence intervals. Our simulation study shows the estimator proposed is usually less biased than customary estimators. The observed coverages of the empirical likelihood confidence interval proposed are also closer to the nominal value.</p>
</abstract>

We propose an estimator for the Gini coefficient, based on a ratio of means. We show how bootstrap and empirical likelihood can be combined to construct confidence intervals. Our simulation study shows the estimator proposed is usually less biased than customary estimators. The observed coverages of the empirical likelihood confidence interval proposed are also closer to the nominal value.

Confidence Intervals of Gini Coefficient Under Unequal Probability Sampling

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Confidence Intervals of Gini Coefficient Under Unequal Probability Sampling"}

We propose an estimator for the Gini coefficient, based on a ratio of means. We show how bootstrap and empirical likelihood can be combined to construct...

Confidence Intervals of Gini Coefficient Under Unequal Probability Sampling

Pubblicato online: 15 giu 2020

Pagine: 237 - 249

Ricevuto: 01 ott 2018

Accettato: 01 dic 2019

DOI: https://doi.org/10.2478/jos-2020-0013

Parole chiave
Bootstrap, empirical likelihood, inclusion probability, survey weight, sampling design

© 2020 Yves G. Berger et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Confidence Intervals of Gini Coefficient Under Unequal Probability Sampling

Pubblicato online: 15 giu 2020

Pagine: 237 - 249

Ricevuto: 01 ott 2018

Accettato: 01 dic 2019

DOI: https://doi.org/10.2478/jos-2020-0013

Parole chiaveBootstrap, empirical likelihood, inclusion probability, survey weight, sampling design

© 2020 Yves G. Berger et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Bootstrap, empirical likelihood, inclusion probability, survey weight, sampling design