Optimal Grouping of Income Distribution Data
研究了如何将收入分配数据分成给定数量的组，使得因分组而隐藏的收入差异最小，通过最小化分组与未分组洛伦兹曲线之间的面积来实现，并讨论了迭代计算方法和分位数分组等应用。
Abstract We consider the problem of grouping income distribution data into a given number of groups such that the concealed income differences due to grouping are minimized. When income differences are measured by Gini's pairwise differences, this means minimizing the area between the grouped and ungrouped Lorenz curves. In this case, the necessary condition for optimal grouping is that each group limit be equal to the average income in its two adjacent groups. An iterative procedure for computation and applications including fractile groupings are discussed. Alternative optimal groupings based on other measures of income differences are also considered. Key Words: Grouped dataOptimal groupingIncome groupsLorenz curveGini indexFractile grouping