The coefficient of variation (CV) is a widely used scaleless measure of variability in many disciplines. However the inference for the CV is limited to parametric methods or standard bootstrap. In this paper we propose two nonparametric methods aiming to construct confidence intervals for the coefficient of variation. The first one is to apply the empirical likelihood after transforming the original data. The second one is a modified jackknife empirical likelihood method. We also propose bootstrap procedures for calibrating the test statistics. Results from our simulation studies suggest that the proposed methods, particularly the empirical likelihood method with bootstrap calibration, are comparable to existing methods for normal data and yield better coverage probabilities for nonnormal data. We illustrate our methods by applying them to two real-life datasets.

中文翻译：

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变异系数的非参数区间估计。

变异系数 (CV) 是许多学科中广泛使用的无标度变异性度量。然而，CV 的推断仅限于参数方法或标准引导程序。在本文中，我们提出了两种非参数方法，旨在构建变异系数的置信区间。第一个是在转换原始数据后应用经验似然。第二种是改进的折刀经验似然法。我们还提出了用于校准测试统计数据的引导程序。我们的模拟研究结果表明，所提出的方法，特别是具有自举校准的经验似然法，与现有的正常数据方法相当，并且为非正常数据产生更好的覆盖概率。