  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.
