# Convex and non-convex regularization methods for spatial point processes intensity estimation

arXiv: https://arxiv.org/abs/1703.02462 · HTML: https://mlsanity.com/paper/1703.02462/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Statistical learning and cross-validation for point processes](https://mlsanity.com/paper/2103.01356/) (2021)
- [Semi-Parametric Inference for Doubly Stochastic Spatial Point Processes: An Approximate Penalized Poisson Likelihood Approach](https://mlsanity.com/paper/2306.06756/) (2023)
- [Robust variable selection for spatial point processes observed with noise](https://mlsanity.com/paper/2510.25550/) (2025)

## Abstract (arXiv)

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.