Two more ways of spelling Gini Coefficient with Applications

TL;DR

This paper introduces two novel definitions and interpretations of the Gini coefficient, demonstrating their application to various distributions and proposing its use for measuring overdispersion in epidemiology.

Contribution

It presents new ways to define and interpret the Gini coefficient, expanding its applicability and understanding in statistical analysis.

Findings

Computed Gini index for several distributions

Detailed analysis for negative binomial distribution

Proposed Gini coefficient as a measure of overdispersion in epidemiology

Abstract

In this paper, we draw attention to a promising yet slightly underestimated measure of variability - the Gini coefficient. We describe two new ways of defining and interpreting this parameter. Using our new representations, we compute the Gini index for a few probability distributions and describe it in more detail for the negative binomial distribution. We also suggest the latter as a tool to measure overdispersion in epidemiology.