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On testing the equality between interquartile ranges

Author

Listed:
  • Luca Greco

    (University Giustino Fortunato)

  • George Luta

    (Georgetown University)

  • Rand Wilcox

    (University of Southern California)

Abstract

The interquartile range is a statistical measure well suited to describe the variability of the data at hand, both at the population level and for sample data. The interquartile range is particularly useful when the distribution of the data is asymmetric or irregularly shaped. Here, the use of the interquartile range is investigated when the main aim is to compare the variability of two distributions using two independent random samples, without the need to make any distributional assumptions. Several techniques are compared through numerical studies and real data examples, with a particular attention given to the use of sample quantiles based on the Harrel-Davis estimator or the quantile regression.

Suggested Citation

  • Luca Greco & George Luta & Rand Wilcox, 2024. "On testing the equality between interquartile ranges," Computational Statistics, Springer, vol. 39(5), pages 2873-2898, July.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:5:d:10.1007_s00180-023-01415-8
    DOI: 10.1007/s00180-023-01415-8
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    References listed on IDEAS

    as
    1. William Gould, 1998. "Interquartile and simultaneous-quantile regression," Stata Technical Bulletin, StataCorp LP, vol. 7(38).
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    3. Hahn, Jinyong, 1995. "Bootstrapping Quantile Regression Estimators," Econometric Theory, Cambridge University Press, vol. 11(1), pages 105-121, February.
    4. Leena Choi & Jeffrey D Blume & William D Dupont, 2015. "Elucidating the Foundations of Statistical Inference with 2 x 2 Tables," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-22, April.
    Full references (including those not matched with items on IDEAS)

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