Bayesian assessment of Lorenz and stochastic dominance
提出一种贝叶斯方法，通过马尔可夫链蒙特卡洛估计两个收入分布的洛伦兹和随机占优后验概率，并应用于印度尼西亚收入数据，用概率曲线解释贫困排序中的占优变化。
Abstract We introduce a Bayesian approach for assessing Lorenz and stochastic dominance. For two income distributions, say X and Y , estimated via Markov chain Monte Carlo, we describe how to compute posterior probabilities for: (i) X dominates Y , (ii) Y dominates X and (iii) neither Y nor X dominates. The proposed approach is applied to Indonesian income distributions using mixtures of gamma densities that ensure flexible modelling. Probability curves depicting the probability of dominance at each population proportion are used to explain changes in dominance probabilities over restricted ranges relevant for poverty orderings. They also explain some seemingly contradictory outcomes from the p ‐values of some sampling theory tests.