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Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
Muhammad Osama · Dave Zachariah · Peter Stoica

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #48

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.

Author Information

Muhammad Osama (Uppsala University)
Dave Zachariah (Uppsala University)
Peter Stoica (Uppsala University)

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