Defining Dispersion: A Fundamental Order for Univariate Discrete Distributions
Andreas Eberl, Bernhard Klar

TL;DR
This paper introduces discrete adaptations of the dispersive order to better measure dispersion in discrete distributions, addressing limitations of traditional measures and ensuring consistency with continuous cases.
Contribution
It defines and analyzes discrete dispersive orders, aligning them with continuous properties and evaluating popular dispersion measures' effectiveness in discrete contexts.
Findings
Discrete dispersive orders are validated as a foundation for discrete dispersion measures.
Most popular dispersion measures preserve the discrete dispersive orders.
Interquantile range does not preserve discrete dispersive orders, making it unsuitable.
Abstract
The measurement of dispersion is one of the most fundamental and ubiquitous statistical concepts, in both applied and theoretical contexts. For dispersion measures, such as the standard deviation, to effectively capture the variability of a given distribution, they must, by definition, preserve some stochastic order of dispersion. The so-called dispersive order is the most basic order that serves as a foundation underneath the concept of dispersion measures. However, this order is incompatible with almost all discrete distributions, including lattice and most empirical distributions. As a result, popular measures may fail to accurately capture the dispersion of such distributions. In this paper, discrete adaptations of the dispersive order are defined and analyzed. They are shown to be a compromise between being equivalent to the original dispersive order on their joint area of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCOVID-19 epidemiological studies · Statistical Methods and Bayesian Inference · Census and Population Estimation
