Spatial point processes intensity estimation with a diverging number of covariates

Abstract : Feature selection procedures for spatial point processes parametric intensity estimation have been recently developed since more and more applications involve a large number of covariates. In this paper, we investigate the setting where the number of covariates diverges as the domain of observation increases. In particular, we consider estimating equations based on Campbell theorems derived from Poisson and logistic regression likelihoods regularized by a general penalty function. We prove that, under some conditions, the consistency, the sparsity, and the asymptotic normality are valid for such a setting. We support the theoretical results by numerical ones obtained from simulation experiments and an application to forestry datasets.
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Contributor : Achmad Choiruddin <>
Submitted on : Wednesday, December 27, 2017 - 1:16:36 PM
Last modification on : Tuesday, November 19, 2019 - 2:56:03 AM
Long-term archiving on: Wednesday, March 28, 2018 - 12:28:02 PM


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  • HAL Id : hal-01672825, version 1
  • ARXIV : 1712.09562



Achmad Choiruddin, Jean-François Coeurjolly, Frédérique Letué. Spatial point processes intensity estimation with a diverging number of covariates. 2017. ⟨hal-01672825⟩



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