Some notes on parametric significance tests for geographically weighted regression
Some notes on parametric significance tests for geographically weighted regression
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DOI：
10.1111/0022-4146.00146
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发表时间：
1999-08-01
影响因子：
3
通讯作者：
Charlton, M
中科院分区：
文献类型：
--
作者：
Brunsdon, C;Fotheringham, AS;Charlton, M
利用地理加权回归（GWR）技术对线性模型系数中的空间“漂移”进行建模。在本文中，我们从多个方面扩展了GWR的思想。首先，我们引入了一组分析推导的显著性检验，允许对无空间参数漂移的零假设进行研究。其次，我们讨论了“混合”GWR模型，其中一些参数是全局固定的，但其他参数在地理上是不同的。同样，这种类型的模型可以使用显著性检验进行评估。最后，我们考虑了一种基于Mallows C-p统计量来确定GWR中参数平滑程度的方法。为了完成本文，我们使用所介绍的技术分析了基于英国肯特郡房价的示例数据集。
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 maybe 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.