Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees

• URL: http://arxiv.org/abs/2007.01592v1
• Date: Fri, 3 Jul 2020 10:11:59 GMT
• Title: Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
• Authors: Muhammad Osama, Dave Zachariah, Petre Stoica
• Abstract summary: 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.
• Score: 23.178396031181393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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