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.
Type de document :
Pré-publication, Document de travail
Liste complète des métadonnées

Littérature citée [47 références]  Voir  Masquer  Télécharger
Contributeur : Achmad Choiruddin <>
Soumis le : mercredi 27 décembre 2017 - 13:16:36
Dernière modification le : mardi 24 avril 2018 - 16:16:02
Document(s) archivé(s) le : mercredi 28 mars 2018 - 12:28:02


Fichiers produits par l'(les) auteur(s)


  • 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〉



Consultations de la notice


Téléchargements de fichiers