论文标题
空间经济学数据的地理加权回归分析：贝叶斯追索权
Geographically Weighted Regression Analysis for Spatial Economics Data: a Bayesian Recourse
论文作者
论文摘要
地理加权回归（GWR）是一种众所周知的统计方法，用于探索空间数据分析中回归关系的空间非平稳性。在本文中，我们讨论了GWR的贝叶斯求助。本文在本文中充分讨论了基于Spike and Slab先验，基于先验范围的带宽选择，以及使用修改的偏差信息标准和修改的伪划分的对数模型评估。还引入了图形距离的使用情况。进行了广泛的仿真研究，以检查所提出的方法的经验性能，这些方法既有位置方案又与大量的位置方案进行了比较，并与经典的频繁主义者进行了比较。在不同情况下，可变选择的性能和提议方法的估计令人满意。我们进一步应用了所提出的方法，分析中国30个选定省的省级宏观经济数据。估计和可变选择结果揭示了有关中国经济的见解，这些见解令人信服并同意以前的研究和事实。
The geographically weighted regression (GWR) is a well-known statistical approach to explore spatial non-stationarity of the regression relationship in spatial data analysis. In this paper, we discuss a Bayesian recourse of GWR. Bayesian variable selection based on spike-and-slab prior, bandwidth selection based on range prior, and model assessment using a modified deviance information criterion and a modified logarithm of pseudo-marginal likelihood are fully discussed in this paper. Usage of the graph distance in modeling areal data is also introduced. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods with both small and large number of location scenarios, and comparison with the classical frequentist GWR is made. The performance of variable selection and estimation of the proposed methodology under different circumstances are satisfactory. We further apply the proposed methodology in analysis of a province-level macroeconomic data of 30 selected provinces in China. The estimation and variable selection results reveal insights about China's economy that are convincing and agree with previous studies and facts.
