论文标题
不均匀空间点过程的信息标准
Information criteria for inhomogeneous spatial point processes
论文作者
论文摘要
许多模型选择标准的理论基础是在不均匀点过程和各种渐近环境下建立的：填充，增加的域和这些组合。对于不均匀的泊松过程，我们考虑了Akaike信息标准和贝叶斯信息标准，尤其是我们确定贝叶斯信息标准所需的样本量的点过程类似物。考虑到一般的非均匀点过程，我们得出了新的复合可能性和复合贝叶斯信息标准，用于选择强度函数的回归模型。使用泊松过程和群集点过程的模拟评估所提出的模型选择标准。
The theoretical foundation for a number of model selection criteria is established in the context of inhomogeneous point processes and under various asymptotic settings: infill, increasing domain, and combinations of these. For inhomogeneous Poisson processes we consider Akaike information criterion and the Bayesian information criterion, and in particular we identify the point process analogue of sample size needed for the Bayesian information criterion. Considering general inhomogeneous point processes we derive new composite likelihood and composite Bayesian information criteria for selecting a regression model for the intensity function. The proposed model selection criteria are evaluated using simulations of Poisson processes and cluster point processes.
