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Robust analogs to the coefficient of variation

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  • Chandima N. P. G. Arachchige
  • Luke A. Prendergast
  • Robert G. Staudte

Abstract

The coefficient of variation (CV) is commonly used to measure relative dispersion. However, since it is based on the sample mean and standard deviation, outliers can adversely affect it. Additionally, for skewed distributions the mean and standard deviation may be difficult to interpret and, consequently, that may also be the case for the ${\rm CV} $CV. Here we investigate the extent to which quantile-based measures of relative dispersion can provide appropriate summary information as an alternative to the CV. In particular, we investigate two measures, the first being the interquartile range (in lieu of the standard deviation), divided by the median (in lieu of the mean), and the second being the median absolute deviation, divided by the median, as robust estimators of relative dispersion. In addition to comparing the influence functions of the competing estimators and their asymptotic biases and variances, we compare interval estimators using simulation studies to assess coverage.

Suggested Citation

  • Chandima N. P. G. Arachchige & Luke A. Prendergast & Robert G. Staudte, 2022. "Robust analogs to the coefficient of variation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(2), pages 268-290, January.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:268-290
    DOI: 10.1080/02664763.2020.1808599
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    Cited by:

    1. Czakon, Wojciech & Klimas, Patrycja & Kawa, Arkadiusz & Kraus, Sascha, 2023. "How myopic are managers? Development and validation of a multidimensional strategic myopia scale," Journal of Business Research, Elsevier, vol. 157(C).
    2. Cao, Yujie & Cheng, Ming & Zhang, Sufang & Mao, Hongju & Wang, Peng & Li, Chao & Feng, Yihui & Ding, Zhaohao, 2022. "Data-driven flexibility assessment for internet data center towards periodic batch workloads," Applied Energy, Elsevier, vol. 324(C).
    3. Perepolkin, Dmytro & Lindsröm, Erik & Sahlin, Ullrika, 2023. "Quantile-parameterized distributions for expert knowledge elicitation," OSF Preprints tq3an, Center for Open Science.

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