Multivariate geometric anisotropic Cox processes
Multivariate geometric anisotropic Cox processes
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DOI：
10.1111/sjos.12640
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发表时间：
2023-05-13
影响因子：
1
通讯作者：
Olhede，Sofia C.
中科院分区：
文献类型：
--
作者：
Martin，James S.;Murrell，David J.;Olhede，Sofia C.
提出了一种新的多变量各向异性点过程的建模和推理框架。基于多元空间统计学的最新创新，我们提出了一个新的多元各向异性随机场族，并从它们中提出了一个各向异性点过程族。我们给出的条件，使所提出的模型有效。我们还提出了一种基于Palm似然的推理方法，用于这种类型的点过程，避免了似然易处理性的问题。最后，我们通过分析巴罗科罗拉多岛数据中植物和树木的空间生态观测来说明所提出的建模框架的实用性。
This paper introduces a new modeling and inference framework for multivariate and anisotropic point processes. Building on recent innovations in multivariate spatial statistics, we propose a new family of multivariate anisotropic random fields, and from them a family of anisotropic point processes. We give conditions that make the proposed models valid. We also propose a Palm likelihood‐based inference method for this type of point process, circumventing issues of likelihood tractability. Finally we illustrate the utility of the proposed modeling framework by analyzing spatial ecological observations of plants and trees in the Barro Colorado Island data.