Some Notes on Parametric Significance Tests for Geographically Weighted Regression
扩展了地理加权回归方法，提出了参数漂移的显著性检验、混合模型检验，以及基于Mallows Cp统计量的平滑度选择，并用英国肯特郡房价数据演示。
The technique of geographically weighted regression (GWR) is used to model spatial ‘drift’ in linear model coefficients. In this paper we extend the ideas of GWR in a number of ways. First, we introduce a set of analytically derived significance tests allowing a null hypothesis of no spatial parameter drift to be investigated. Second, we discuss ‘mixed’ GWR models where some parameters are fixed globally but others vary geographically. Again, models of this type may be assessed using significance tests. Finally, we consider a means of deciding the degree of parameter smoothing used in GWR based on the Mallows C p statistic. To complete the paper, we analyze an example data set based on house prices in Kent in the U.K. using the techniques introduced.