Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for a class of multivariate log Gaussian Cox processes. The methodology is applied to a highly multivariate point pattern data set from tropical rain forest ecology.

中文翻译：

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高多元对数高斯考克斯过程的正则估计

由于具有大量参数的复杂模型，高度多元点模式数据的统计推断颇具挑战性。在本文中，我们为一类多元对数高斯Cox过程开发了数值稳定，有效的参数估计和模型选择算法。该方法应用于来自热带雨林生态学的高度多元的点模式数据集。