We propose a general framework for Maximum Likelihood (ML) and Bayesian estimation of income distributions based on grouped data information. The asymptotic properties of the ML estimators are derived and Bayesian parameter estimates are obtained by Monte Carlo Markov Chain (MCMC) techniques. A comprehensive simulation experiment shows that obtained estimates of the income distribution are very precise and that the proposed estimation framework improves the statistical precision of parameter estimates relative to the classical multinomial likelihood. The estimation approach is finally applied to a set of countries included in the World Bank database PovcalNet.

中文翻译：

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使用分组数据进行收入分配的经典和贝叶斯推断

我们提出了基于分组数据信息的最大可能性（ML）和收入分配的贝叶斯估计的通用框架。通过蒙特卡洛·马尔可夫链（MCMC）技术推导了ML估计量的渐近性质，并获得了贝叶斯参数估计量。全面的模拟实验表明，所获得的收入分配估计值非常精确，并且所提出的估计框架相对于经典多项式似然性提高了参数估计的统计精度。估算方法最终应用于世界银行数据库PovcalNet中包含的一组国家。