论文标题
贝叶斯对数正态分布的混合物的分析，该分布数量未知数来自分组数据
Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data
论文作者
论文摘要
这项研究提出了一种可逆的跳跃马尔可夫链蒙特卡洛方法，用于估计收入的对数正态分布混合物的参数。使用模拟数据示例，我们检查了所提出的算法的性能以及Gini系数后验分布的准确性。结果表明，准确估计参数。因此，即使考虑到不同的数据生成过程，后验分布也接近真实分布。此外，Gini系数的有希望的结果鼓励我们将我们的方法应用于日本的真实数据。经验实例表明日本的两个亚组（2020）和Gini系数的完整性。
This study proposes a reversible jump Markov chain Monte Carlo method for estimating parameters of lognormal distribution mixtures for income. Using simulated data examples, we examined the proposed algorithm's performance and the accuracy of posterior distributions of the Gini coefficients. Results suggest that the parameters were estimated accurately. Therefore, the posterior distributions are close to the true distributions even when the different data generating process is accounted for. Moreover, promising results for Gini coefficients encouraged us to apply our method to real data from Japan. The empirical examples indicate two subgroups in Japan (2020) and the Gini coefficients' integrity.
