We study an iterative discrete information production process (IPP) where we can extend ordered normalised vectors by new elements based on a simple affine transformation, while preserving the predefined level of inequality, G, as measured by the Gini index. Then, we derive the family of Lorenz curves of the corresponding vectors and prove that it is stochastically ordered with respect to both the sample size and G which plays the role of the uncertainty parameter. A case study of family income data in nine countries shows a very good fit of our model. Moreover, we show that asymptotically, we obtain all, and only, Lorenz curves generated by a new, intuitive parametrisation of the finite-mean Generalised Pareto Distribution (GPD) that unifies three other families, namely: the Pareto Type II, exponential, and scaled beta ones. The family is not only ordered with respect to the parameter G, but also, thanks to our derivations, has a nice underlying interpretation. Our result may thus shed new light on the genesis of this family of distributions.
翻译:我们研究了一种迭代离散信息生成过程(IPP),其中我们可以基于简单的仿射变换将有序的归一化向量扩展到新元素,同时保持由基尼指数测量的不平等水平 G。然后,我们推导了相应向量的洛伦兹曲线家族,并证明了它在样本大小和 G(扮演不确定性参数的角色)方面都随机有序。九个国家家庭收入数据的案例研究显示了我们模型的非常好的拟合性。此外,我们展示由于我们的推导,渐近地,我们得到了所有的、也只有新的直观参数化的具有有限均值的广义帕累托分布(GPD)生成的洛伦兹曲线,该分布统一了另外三个家族,包括 Pareto Type II,指数和缩放 beta 分布。该家族不仅在参数 G 方面有序,而且由于我们的推导,具有良好的基础解释。我们的结果可能为这个分布族的发展提供新的视角。