research area Data Science and Annotationconference International Conference on Advanced Data Mining and Applications (ADMA)
content type paperpublished September 2025

Alternative Statistical Inference for the First Normalized Incomplete Moment

AuthorsJiannan Lu, Peng Ding†, Anqi Zhao‡

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This paper re-examines the first normalized incomplete moment, a well-established measure of inequality with wide applications in economic and social sciences. Despite the popularity of the measure itself, existing statistical inference appears to lag behind the needs of modern-age analytics. To fill this gap, we propose an alternative solution that is intuitive, computationally efficient, mathematically equivalent to the existing solutions for “standard” cases, and easily adaptable to “non-standard” ones. The theoretical and practical advantages of the proposed methodology are demonstrated via both simulated and real-life examples. In particular, we discover that a common practice in industry can lead to highly non-trivial challenges for trustworthy statistical inference, or misleading decision making altogether.

  • † Department of Statistics, University of California, Berkeley
  • ‡ Fuqua School of Business, Duke University

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