Mechanistic spatio-temporal point process models for marked point processes, with a view to forest stand data

Jesper Møller, Mohammad Ghorbani and Ege Rubak

Biometrics, 2016, vol. 72, issue 3, 687-696

Abstract: type="main" xml:lang="en">

We show how a spatial point process, where to each point there is associated a random quantitative mark, can be identified with a spatio-temporal point process specified by a conditional intensity function. For instance, the points can be tree locations, the marks can express the size of trees, and the conditional intensity function can describe the distribution of a tree (i.e., its location and size) conditionally on the larger trees. This enable us to construct parametric statistical models which are easily interpretable and where maximum-likelihood-based inference is tractable.

Date: 2016
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