论文标题
基于分组数据的时空平滑，插值和预测收入分布
Spatio-temporal smoothing, interpolation and prediction of income distributions based on grouped data
论文作者
论文摘要
日本的住房和土地调查（HLS）提供了有关家庭收入的市政级分组数据。尽管这些数据可用于有效的本地决策，但它们的分析受到了几个挑战的阻碍，例如归因于分组的有限信息，非采样区域的存在以及实施调查的频率非常低。为了应对这些挑战，我们提出了一种新型的基于DATA的新型时空有限混合模型，用于估计多个时间点上多个空间单位的收入分布。该方法的一个独特特征是，所有区域都具有共同的潜在分布，并且包括空间和时间效应在内的混合比例捕获了潜在面积的异质性。因此，结合这些效果可以使兴趣数量随时间和空间的含量平滑，估算缺失值并预测未来的价值。通过使用提出的方法处理HLS数据，我们在任意时间获得收入和不平等措施的完整地图，这可以促进具有良好粒度的快速有效决策。
The Housing and Land Survey (HLS) of Japan provides municipality-level grouped data on household incomes. Although these data can be used for effective local policymaking, their analyses are hindered by several challenges, such as limited information attributed to grouping, the presence of non-sampled areas, and the very low frequency of implementing surveys. To address these challenges, we propose a novel grouped-data-based spatio-temporal finite mixture model for estimating the income distributions of multiple spatial units at multiple time points. A unique feature of the proposed method is that all the areas share common latent distributions and that the mixing proportions, including spatial and temporal effects, capture the potential area-wise heterogeneity. Thus, incorporating these effects can smooth out the quantities of interest over time and space, impute missing values, and predict future values. By treating the HLS data with the proposed method, we obtain complete maps of the income and inequality measures at an arbitrary time, which can facilitate rapid and efficient policymaking with fine granularity.
