论文标题
空间点过程的预测：带有样本外担保的正则化方法
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
论文作者
论文摘要
空间点过程可以以强度函数为特征，该强度函数可以预测跨空间发生的事件数量。在本文中，我们通过使用正规标准学习空间模型来开发一种推断预测强度间隔的方法。我们证明，所提出的方法表现出样本外的预测性能保证，与标准估计器不同，即使空间模型被误指定，也是有效的。使用合成和实际空间数据证明该方法。
A spatial point process can be characterized by an intensity function which predicts the number of events that occur across space. In this paper, we develop a method to infer predictive intensity intervals by learning a spatial model using a regularized criterion. We prove that the proposed method exhibits out-of-sample prediction performance guarantees which, unlike standard estimators, are valid even when the spatial model is misspecified. The method is demonstrated using synthetic as well as real spatial data.
