Wang Dongliang, Formica Margaret K, Liu Song
Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, Syracuse, NYUSA.
Int J Biostat. 2018 Apr 19;14(1):ijb-2017-0041. doi: 10.1515/ijb-2017-0041.
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.
变异系数（CV）是许多学科中广泛使用的无量纲变异性度量。然而，对CV的推断仅限于参数方法或标准自助法。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。第一种方法是在对原始数据进行变换后应用经验似然法。第二种方法是一种改进的刀切经验似然法。我们还提出了用于校准检验统计量的自助程序。我们的模拟研究结果表明，所提出的方法，特别是经过自助校准的经验似然法，对于正态数据与现有方法相当，对于非正态数据具有更好的覆盖概率。我们通过将这些方法应用于两个实际数据集来说明我们的方法。
