Title: Convex and non-convex regularization methods for spatial point processes intensity estimation,
Abstract: This paper deals with feature selection procedures for spatial point
processes intensity estimation. We consider regularized versions of estimating
equations based on Campbell theorem derived from two classical functions:
Poisson likelihood and logistic regression likelihood. We provide general
conditions on the spatial point processes and on penalty functions which ensure
consistency, sparsity and asymptotic normality. We discuss the numerical
implementation and assess finite sample properties in a simulation study.
Finally, an application to tropical forestry datasets illustrates the use of
the proposed methods.