分组数据Lorenz曲线的近似贝叶斯计算
Approximate Bayesian Computation for Lorenz Curves from Grouped Data
摘要：
提出了一种新的基于分组数据的Lorenz曲线估计基尼系数的贝叶斯方法。所提出的方法假设一个假设的收入分布和估计的参数直接工作的似然函数隐含的洛伦兹曲线的收入分布从分组数据。它继承了现有的两种方法的优点，通过这两种方法可以更准确地估计基尼系数，并提供了一个简单的解释有关的收入分配。由于似然函数是隐式定义的，因此采用了基于序贯蒙特卡罗方法的近似贝叶斯计算方法。通过模拟研究和日本的收入数据说明了所提出的方法的实用性。
英文摘要：
This paper proposes a new Bayesian approach to estimate the Gini coefficient from the Lorenz curve based on grouped data. The proposed approach assumes a hypothetical income distribution and estimates the parameter by directly working on the likelihood function implied by the Lorenz curve of the income distribution from the grouped data. It inherits the advantages of two existing approaches through which the Gini coefficient can be estimated more accurately and a straightforward interpretation about the underlying income distribution is provided. Since the likelihood function is implicitly defined, the approximate Bayesian computational approach based on the sequential Monte Carlo method is adopted. The usefulness of the proposed approach is illustrated through the simulation study and the Japanese income data.
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