Posterior distributions for the Gini coefficient using grouped data
Posterior distributions for the Gini coefficient using grouped data
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DOI：
10.1111/1467-842x.00136
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发表时间：
2000-12-01
影响因子：
1.1
通讯作者：
Griffiths, WE
中科院分区：
文献类型：
--
作者：
Chotikapanich, D;Griffiths, WE
当现有数据包括若干收入类别中每一类别的若干抽样住户时，似然函数是从以收入类别人口比例为未知参数的多项分布中获得的。从这个似然函数的基尼系数的后验分布的两种方法进行了研究。在第一种方法中，考虑了关于潜在收入分布的两个替代假设，即对数正态分布和Singh-Maddala（1976）收入分布。在这些情况下，似然函数被重新参数化，基尼系数是收入分布参数的非线性函数。大都会算法是用来找到相应的后验分布的基尼系数从样本的曼谷家庭。第二种方法不需要假设收入分布的性质，但使用（a）三角先验分布，（B）贝塔先验分布，在每个收入类别中的平均收入的位置。通过对这些分布进行抽样，以及收入阶层比例的Dirichlet后验分布，计算了基尼系数的替代后验分布。
When available data comprise a number of sampled households in each of a number of income classes, the likelihood function is obtained from a multinomial distribution with the income class population proportions as the unknown parameters. Two methods for going from this likelihood function to a posterior distribution on the Gini coefficient are investigated. In the first method, two alternative assumptions about the underlying income distribution are considered, namely a lognormal distribution and the Singh-Maddala (1976) income distribution. In these cases the likelihood function is reparameterized and the Gini coefficient is a nonlinear function of the income distribution parameters. The Metropolis algorithm is used to find the corresponding posterior distributions of the Gini coefficient from a sample of Bangkok households. The second method does not require an assumption about the nature of the income distribution, but uses (a) triangular prior distributions, and (b) beta prior distributions, on the location of mean income within each income class. By sampling from these distributions, and the Dirichlet posterior distribution of the income class proportions, alternative posterior distributions of the Gini coefficient are calculated.