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Exact moment formulae for mean-normalized statistics with applications to inequality measures

Haolin Zou et al.

Statistics & Probability Letters2026https://doi.org/10.1016/j.spl.2026.110754article
AJG 2ABDC B
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0.50

What the paper says

We study a group of self-normalized statistics formed as ratios involving powers of the sample mean with non-negative data, whose distributional properties are largely unknown. We derive a unified moment formula that is computationally scalable and yields concise expressions in key cases of inequality measures in economics, such as the Theil index, the Generalized Entropy Index and the Gini Index. Theory and simulations highlight their bias and variance, and we propose a plug-in debiasing method with applications to these statistics.

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https://doi.org/https://doi.org/10.1016/j.spl.2026.110754

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@article{haolin2026,
  title        = {{Exact moment formulae for mean-normalized statistics with applications to inequality measures}},
  author       = {Haolin Zou et al.},
  journal      = {Statistics & Probability Letters},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.spl.2026.110754},
}

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Exact moment formulae for mean-normalized statistics with applications to inequality measures

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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