Logistic regression for spatial Gibbs point processes
Logistic regression for spatial Gibbs point processes
复制标题
空间吉布斯点过程的逻辑回归
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
R. Waagepetersen
中科院分区：
文献类型：
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作者：
A. Baddeley;Jean‐François Coeurjolly;E. Rubak;R. Waagepetersen
我们提出了一种计算效率高的技术，基于逻辑回归，拟合吉布斯点过程模型的空间点模式数据。Logistic回归的得分是一个无偏估计函数，与伪概率得分密切相关。我们的技术的实现不需要数值求积，从而避免了其他方法中固有的偏差源。对于平稳过程，我们证明了参数估计是强相合和渐近正态的，并提出了一个方差估计。我们证明了该方法的效率和实用性的真实的数据集和模拟研究。
We propose a computationally efficient technique, based on logistic regression, for fitting Gibbs point process models to spatial point pattern data. The score of the logistic regression is an unbiased estimating function and is closely related to the pseudolikelihood score. Implementation of our technique does not require numerical quadrature, and thus avoids a source of bias inherent in other methods. For stationary processes, we prove that the parameter estimator is strongly consistent and asymptotically normal, and propose a variance estimator. We demonstrate the efficiency and practicability of the method on a real dataset and in a simulation study.