AI 中文总结
本文针对空间点过程中标记间结构关系，将距离协方差与距离相关调整至标记点过程，提出适用于实值、多元及非标量标记的新型标记特征类。
AI 中文摘要
随着数据采集设备和存储容量的快速发展，我们能够获取越来越多的空间点模式数据，其中每个事件位置都附带有多个、可能非标量的标记。因此，需要高效的分析技术来研究标记之间的结构关系。在本文中，我们回顾了距离协方差和距离相关，并将其调整到标记点过程设置中。由此，我们引入了一类新颖的标记特征，适用于单一实值标记以及多元标记组合，包括整数和实值量的混合以及非标量标记。
英文摘要
With the rapid advancement in data collection devices and storage capacities, we have access to increasing amount of spatial point pattern data where each event location is augmented by multiple, potentially non-scalar marks. Therefore, there is need for efficient analysis techniques to investigate the structural relationships between the marks. In this paper, we recall the distance covariance and distance correlation and adjust them to the marked point process setting. As a result, we introduce a novel class of mark characteristics for single real-valued marks as well as multivariate combinations of marks, including mixtures of integer- and real-valued quantities, and non-scalar marks.
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