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Mario Schlemmer

Publications and source records attributed to Mario Schlemmer.

3 recordsLinked to original sources

A robust measure of skewness using cumulative statistic calculation

An important aspect of the shape of a distribution is the level of asymmetry. Strong asymmetries play a role in many ecosystems and are found in the size and reproductive success of individuals. But the standard third moment coefficient of skewness has the drawback that it is very sensitive to outliers, which can lead to incorrect interpretations. A new metric is introduced that is based on calculating the cumulative statistics of the Lorenz curve framework, but it evaluates the asymmetry of the underlying distribution. The standard requirements for skewness measures which the proposed measure satisfies are briefly described and it is compared to the moment-based measure using the lognormal distribution with and without outliers. The results demonstrate that the proposed measure behaves similarly for 'normal' distributions, but is robust(not overly sensitive) if there are outliers.

stat.ME

Including the asymmetry of the Lorenz curve into measures of economic inequality

The Gini index signals only the dispersion of the distribution and is not very sensitive to income differences at the tails of the distribution. The widely used index of inequality can be adjusted to also measure distributional asymmetry by attaching weights to the distances between the Lorenz curve and the 45-degree line. The measure is equivalent to the Gini if the distribution is symmetric. The alternative measure of inequality inherits good properties from the Gini but is more sensitive to changes in the extremes of the income distribution.

econ.EM

Coupling the Gini and Angles to Evaluate Economic Dispersion

Classical measures of inequality use the mean as the benchmark of economic dispersion. They are not sensitive to inequality at the left tail of the distribution, where it would matter most. This paper presents a new inequality measurement tool that gives more weight to inequality at the lower end of the distribution, it is based on the comparison of all value pairs and synthesizes the dispersion of the whole distribution. The differences that sum to the Gini coefficient are scaled by angular differences between observations. The resulting index possesses a set of desirable properties, including normalization, scale invariance, population invariance, transfer sensitivity, and weak decomposability.

econ.EM