发帖
雷达卡
摘要翻译：
空间关联性和异质性是空间分析、地理学、统计学等学科研究的两个重要领域。虽然人们提出和研究了大量优秀的方法，但很少有人倾向于研究异构环境下的空间关联。另外，传统的方法大多基于距离统计和空间加权矩阵。然而，在一些抽象的空间情况下，距离统计无法应用，因为我们甚至不能直接观察到地理位置。同时，在这种情况下，由于空间位置的不可见性，权重矩阵的设计也不能完全避免主观性。本文提出了一种新的基于熵的方法，该方法是数据驱动的、无分布的，可以帮助我们在充分考虑异质性广泛存在的情况下研究空间关联。具体地说，该方法不受距离统计量和权重矩阵的限制。采用非对称相关性来反映个体在空间关联上的异质性，本文的全部讨论都是在时空数据上进行的，只假设随时间平稳的m-相关。
---
英文标题：
《A Nonparametric Approach to Measure the Heterogeneous Spatial
Association: Under Spatial Temporal Data》
---
作者：
Zihao Yuan
---
最新提交年份：
2018
---
分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
---
英文摘要：
Spatial association and heterogeneity are two critical areas in the research about spatial analysis, geography, statistics and so on. Though large amounts of outstanding methods has been proposed and studied, there are few of them tend to study spatial association under heterogeneous environment. Additionally, most of the traditional methods are based on distance statistic and spatial weighted matrix. However, in some abstract spatial situations, distance statistic can not be applied since we can not even observe the geographical locations directly. Meanwhile, under these circumstances, due to invisibility of spatial positions, designing of weight matrix can not absolutely avoid subjectivity. In this paper, a new entropy-based method, which is data-driven and distribution-free, has been proposed to help us investigate spatial association while fully taking the fact that heterogeneity widely exist. Specifically, this method is not bounded with distance statistic or weight matrix. Asymmetrical dependence is adopted to reflect the heterogeneity in spatial association for each individual and the whole discussion in this paper is performed on spatio-temporal data with only assuming stationary m-dependent over time.
---
PDF链接：
https://arxiv.org/pdf/1803.02334
京公网安备 11010802022788号
