2017/12/27 by Achmad Choiruddin, Choiruddin, Achmad, Jean‐François Coeurjolly +3
Engineering · Environmental Science · Mathematics · #3D Shape Modeling and Analysis #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Point processes and geometric inequalities #Remote Sensing and LiDAR Applications #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1712.09562
openalex publication_date 2017/12/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.