论文标题
两个变化系数的比较：一种新的贝叶斯方法
Comparison of two coefficients of variation: a new Bayesian approach
论文作者
论文摘要
变化系数是比较具有不同单位或广泛不同方式的数据集之间值的传播的有用指标。在本文中，我们解决了研究两个独立人群变异系数的平等的问题。为了做到这一点，我们依靠最近在文献中引入的贝叶斯差异措施。当变异系数是分布的单个参数的函数时，计算此贝叶斯的证据度量很简单。相反，当它是更多参数的函数时，通常需要使用MCMC方法，这变得困难。我们通过考虑多种分布来计算贝叶斯差异度量，这些分布的变化系数取决于多个参数。我们还考虑对真实数据的应用。据我们所知，文献中尚未涵盖一些研究的问题。
The coefficient of variation is a useful indicator for comparing the spread of values between dataset with different units or widely different means. In this paper we address the problem of investigating the equality of the coefficients of variation from two independent populations. In order to do this we rely on the Bayesian Discrepancy Measure recently introduced in the literature. Computing this Bayesian measure of evidence is straightforward when the coefficient of variation is a function of a single parameter of the distribution. In contrast, it becomes difficult when it is a function of more parameters, often requiring the use of MCMC methods. We calculate the Bayesian Discrepancy Measure by considering a variety of distributions whose coefficients of variation depend on more than one parameter. We consider also applications to real data. As far as we know, some of the examined problems have not yet been covered in the literature.
