Functional summary statistics and testing for independence in multi-type point processes on the surface of three dimensional convex shapes

IF 2.1 2区 数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY
S. Ward, E.A.K. Cohen, N.M. Adams
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引用次数: 0

Abstract

The fundamental functional summary statistics used for studying spatial point patterns are developed for marked homogeneous and inhomogeneous point processes on the surface of a sphere. These are extended to point processes on the surface of three dimensional convex shapes given the bijective mapping from the shape to the sphere is known. These functional summary statistics are used to test for independence between the marginals of multi-type spatial point processes with methods for sampling the null distribution developed and discussed. This is illustrated on both simulated data and the RNGC galaxy point pattern, revealing attractive dependencies between different galaxy types.
三维凸形表面多类型点加工的功能汇总统计与独立性检验
针对球面上有标记的齐次和非齐次点过程,建立了用于空间点模式研究的基本功能汇总统计量。这些扩展到三维凸形状表面上的点过程,给定从形状到球体的双射映射是已知的。这些功能汇总统计数据用于检验多类型空间点过程的边缘之间的独立性,并开发和讨论了对零分布进行抽样的方法。模拟数据和RNGC星系点图都说明了这一点,揭示了不同星系类型之间的吸引力依赖关系。
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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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