论文标题
在不同系数建模中平衡空间和非空间变化：一种伪造相关的补救措施
Balancing spatial and non-spatial variation in varying coefficient modeling: a remedy for spurious correlation
论文作者
论文摘要
这项研究讨论了在空间回归建模中平衡空间和非空间变化的重要性。与空间变化的系数（SVC）建模不同，在空间统计中很受欢迎，非空间变化的系数（NVC）建模在空间领域中基本上尚未探索。然而，正如我们将要解释的那样，考虑非空间变化的考虑不仅需要提高模型的准确性，而且还需要减少不同系数之间的虚假相关性，这是SVC建模的主要问题。我们考虑一种Moran特征向量方法在空间和非空间变化的系数上建模（S＆NVC）。将我们的S＆NVC模型与现有SVC模型进行比较的Monte Carlo模拟实验，提出了我们方法的建模准确性和计算效率。除此之外，令人惊讶的是，即使通常的SVC模型遭受了严重的虚假相关性，我们的方法几乎完美地确定了系数之间的真实和虚假相关性。这意味着即使分析目的是对SVC进行建模，也应使用S＆NVC模型。最后，我们的S＆NVC模型用于分析住宅土地价格数据集。它的结果表明，在实践中，回归系数中存在空间和非空间变化。现在在R软件包SPMORAN中实现了S＆NVC模型。
This study discusses the importance of balancing spatial and non-spatial variation in spatial regression modeling. Unlike spatially varying coefficients (SVC) modeling, which is popular in spatial statistics, non-spatially varying coefficients (NVC) modeling has largely been unexplored in spatial fields. Nevertheless, as we will explain, consideration of non-spatial variation is needed not only to improve model accuracy but also to reduce spurious correlation among varying coefficients, which is a major problem in SVC modeling. We consider a Moran eigenvector approach modeling spatially and non-spatially varying coefficients (S&NVC). A Monte Carlo simulation experiment comparing our S&NVC model with existing SVC models suggests both modeling accuracy and computational efficiency for our approach. Beyond that, somewhat surprisingly, our approach identifies true and spurious correlations among coefficients nearly perfectly, even when usual SVC models suffer from severe spurious correlations. It implies that S&NVC model should be used even when the analysis purpose is modeling SVCs. Finally, our S&NVC model is employed to analyze a residential land price dataset. Its results suggest existence of both spatial and non-spatial variation in regression coefficients in practice. The S&NVC model is now implemented in the R package spmoran.
