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 $\mathrm{CV}$. 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.

变异系数(CV)通常用于测量相对色散。但是，由于它基于样本均值和标准差，因此异常值会对它产生不利影响。此外，对于偏态分布，均值和标准差可能难以解释，因此，对于$\mathrm{简历}$. 在这里，我们研究了基于分位数的相对分散测量在多大程度上可以提供适当的摘要信息作为 CV 的替代方案。特别是，我们研究了两个度量，第一个是四分位距（代替标准差）除以中位数（代替平均值），第二个是中位数绝对偏差，除以中位数，如相对分散的稳健估计。除了比较竞争估计量的影响函数及其渐近偏差和方差外，我们还使用模拟研究来比较区间估计量以评估覆盖率。