Brazilian Journal of Probability and Statistics

Spatiotemporal point processes: regression, model specifications and future directions

Dani Gamerman

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Point processes are one of the most commonly encountered observation processes in Spatial Statistics. Model-based inference for them depends on the likelihood function. In the most standard setting of Poisson processes, the likelihood depends on the intensity function, and can not be computed analytically. A number of approximating techniques have been proposed to handle this difficulty. In this paper, we review recent work on exact solutions that solve this problem without resorting to approximations. The presentation concentrates more heavily on discrete time but also considers continuous time. The solutions are based on model specifications that impose smoothness constraints on the intensity function. We also review approaches to include a regression component and different ways to accommodate it while accounting for additional heterogeneity. Applications are provided to illustrate the results. Finally, we discuss possible extensions to account for discontinuities and/or jumps in the intensity function.

Article information

Braz. J. Probab. Stat., Volume 33, Number 4 (2019), 686-705.

Received: February 2019
Accepted: April 2019
First available in Project Euclid: 26 August 2019

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Zentralblatt MATH identifier

Data augmentation discretization dynamic Gaussian processes partition models spatial interpolation


Gamerman, Dani. Spatiotemporal point processes: regression, model specifications and future directions. Braz. J. Probab. Stat. 33 (2019), no. 4, 686--705. doi:10.1214/19-BJPS444.

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