FINITE-SAMPLE MOMENTS OF THE COEFFICIENT OF VARIATION
研究了在一般分布下样本变异系数的有限样本偏差和均方误差，使用Nagar型展开和二次型矩推导结果，发现近似偏差依赖于分布的偏度和峰度，近似均方误差依赖于高达六阶的累积量。
We study the finite-sample bias and mean squared error, when properly defined, of the sample coefficient of variation under a general distribution. We employ a Nagar-type expansion and use moments of quadratic forms to derive the results. We find that the approximate bias depends on not only the skewness but also the kurtosis of the distribution, whereas the approximate mean squared error depends on the cumulants up to order 6.