The Advantages of Using Group Means in Estimating the Lorenz Curve and Gini Index From Grouped Data

DOI10.1080/00031305.2015.1105152OpenAlexW2332360786WikidataQ58259718 ScholiaQ58259718MaRDI QIDQ5884408

Joseph L. Gastwirth, Merritt Lyon, Li C. Cheung

Publication date: 21 March 2023

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00031305.2015.1105152

zbMATH Keywords

interpolationgrouped dataGini indexLorenz curvegroup meanssplit histogram

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (7)

A Simple and Effective Inequality Measure ⋮ The Lorenz Curve in the Classroom ⋮ Confidence intervals for quantiles from histograms and other grouped data ⋮ A new computational approach for estimation of the Gini index based on grouped data ⋮ From grouped to de-grouped data: a new approach in distribution fitting for grouped data ⋮ Interval Estimators for Inequality Measures Using Grouped Data ⋮ EM algorithm for mixture of skew-normal distributions fitted to grouped data

Cites Work

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• Applying the Lorenz curve to disease risk to optimize health benefits under cost constraints
• The Gini methodology. A primer on a statistical methodology.
• Summarizing Insurance Scores Using a Gini Index
• The Estimation and Interpolation of Inequality Measures
• Statistical Size Distributions in Economics and Actuarial Sciences
• A General Definition of the Lorenz Curve
• Histogram-Based Interpolation of the Lorenz Curve and Gini Index for Grouped Data

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