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Estimation of the Gini coefficient for the lognormal distribution of income using the Lorenz curve
The main objective of the study is to compare the Newton–Cotes methods such as the Trapezium rule, Simpson 1/3 rule and Simpson 3/8 rule to estimate the area under the Lorenz curve and Gini coefficient of income using polynomial function with degree 5. Comparing the Gini coefficients of income compu...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4963343/ https://www.ncbi.nlm.nih.gov/pubmed/27516934 http://dx.doi.org/10.1186/s40064-016-2868-z |
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author | Darkwah, Kwasi A. Nortey, Ezekiel N. N. Lotsi, Anani |
author_facet | Darkwah, Kwasi A. Nortey, Ezekiel N. N. Lotsi, Anani |
author_sort | Darkwah, Kwasi A. |
collection | PubMed |
description | The main objective of the study is to compare the Newton–Cotes methods such as the Trapezium rule, Simpson 1/3 rule and Simpson 3/8 rule to estimate the area under the Lorenz curve and Gini coefficient of income using polynomial function with degree 5. Comparing the Gini coefficients of income computed from the Polynomial function with degree 5 for the Trapezium, Simpson 1/3 and Simpson 3/8 methods using the relative errors showed that the trapezium rule, Simpson’s 1/3 rule and Simpson’s 3/8 rule show negative biases with the Simpson 1/3 rule yielding the lowest absolute relative true error of 4.230711 %. |
format | Online Article Text |
id | pubmed-4963343 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-49633432016-08-11 Estimation of the Gini coefficient for the lognormal distribution of income using the Lorenz curve Darkwah, Kwasi A. Nortey, Ezekiel N. N. Lotsi, Anani Springerplus Research The main objective of the study is to compare the Newton–Cotes methods such as the Trapezium rule, Simpson 1/3 rule and Simpson 3/8 rule to estimate the area under the Lorenz curve and Gini coefficient of income using polynomial function with degree 5. Comparing the Gini coefficients of income computed from the Polynomial function with degree 5 for the Trapezium, Simpson 1/3 and Simpson 3/8 methods using the relative errors showed that the trapezium rule, Simpson’s 1/3 rule and Simpson’s 3/8 rule show negative biases with the Simpson 1/3 rule yielding the lowest absolute relative true error of 4.230711 %. Springer International Publishing 2016-07-28 /pmc/articles/PMC4963343/ /pubmed/27516934 http://dx.doi.org/10.1186/s40064-016-2868-z Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Darkwah, Kwasi A. Nortey, Ezekiel N. N. Lotsi, Anani Estimation of the Gini coefficient for the lognormal distribution of income using the Lorenz curve |
title | Estimation of the Gini coefficient for the lognormal distribution of income using the Lorenz curve |
title_full | Estimation of the Gini coefficient for the lognormal distribution of income using the Lorenz curve |
title_fullStr | Estimation of the Gini coefficient for the lognormal distribution of income using the Lorenz curve |
title_full_unstemmed | Estimation of the Gini coefficient for the lognormal distribution of income using the Lorenz curve |
title_short | Estimation of the Gini coefficient for the lognormal distribution of income using the Lorenz curve |
title_sort | estimation of the gini coefficient for the lognormal distribution of income using the lorenz curve |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4963343/ https://www.ncbi.nlm.nih.gov/pubmed/27516934 http://dx.doi.org/10.1186/s40064-016-2868-z |
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