利用空间序数模式对空间依赖性进行非参数检验

IF 2.1 2区 数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY
Christian H. Weiß , Hee-Young Kim
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引用次数: 0

摘要

我们根据空间序数模式(SOPs)来分析发生在规则二维网格中的数据的空间依赖性。在推导出空间独立性零假设下 SOP 频率的渐近分布后,我们使用 SOP 类型的概念来定义检验空间依赖性的统计量。所提出的检验方法不仅适用于实值随机变量,也适用于平面上的离散值空间过程。考虑到各种数据生成过程,我们通过模拟全面分析了空间依赖性检验的性能。结果表明,基于 SOP 的依赖性检验具有良好的尺寸特性,是对空间自相关函数的重要和有价值的补充。更具体地说,基于 SOP 的检验可以检测非线性过程中的空间依赖性,而且对异常值和零膨胀具有稳健性。为了说明它们在实践中的应用,我们分析了两个来自农业科学领域的实际数据实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using spatial ordinal patterns for non-parametric testing of spatial dependence

We analyze data occurring in a regular two-dimensional grid for spatial dependence based on spatial ordinal patterns (SOPs). After having derived the asymptotic distribution of the SOP frequencies under the null hypothesis of spatial independence, we use the concept of the type of SOPs to define the statistics to test for spatial dependence. The proposed tests are not only implemented for real-valued random variables, but a solution for discrete-valued spatial processes in the plane is provided as well. The performances of the spatial-dependence tests are comprehensively analyzed by simulations, considering various data-generating processes. The results show that SOP-based dependence tests have good size properties and constitute an important and valuable complement to the spatial autocorrelation function. To be more specific, SOP-based tests can detect spatial dependence in non-linear processes, and they are robust with respect to outliers and zero inflation. To illustrate their application in practice, two real-world data examples from agricultural sciences are analyzed.

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来源期刊
Spatial Statistics
Spatial Statistics GEOSCIENCES, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.00
自引率
21.70%
发文量
89
审稿时长
55 days
期刊介绍: Spatial Statistics publishes articles on the theory and application of spatial and spatio-temporal statistics. It favours manuscripts that present theory generated by new applications, or in which new theory is applied to an important practical case. A purely theoretical study will only rarely be accepted. Pure case studies without methodological development are not acceptable for publication. Spatial statistics concerns the quantitative analysis of spatial and spatio-temporal data, including their statistical dependencies, accuracy and uncertainties. Methodology for spatial statistics is typically found in probability theory, stochastic modelling and mathematical statistics as well as in information science. Spatial statistics is used in mapping, assessing spatial data quality, sampling design optimisation, modelling of dependence structures, and drawing of valid inference from a limited set of spatio-temporal data.
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