# Adaptive lasso and Dantzig selector for spatial point processes intensity estimation

arXiv: https://arxiv.org/abs/2101.03698 · HTML: https://mlsanity.com/paper/2101.03698/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2021.

## Built on by (most cited first, 2 of 2)

- [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)

Lasso and Dantzig selector are standard procedures able to perform variable selection and estimation simultaneously. This paper is concerned with extending these procedures to spatial point process intensity estimation. We propose adaptive versions of these procedures, develop efficient computational methodologies and derive asymptotic results for a large class of spatial point processes under an original setting where the number of parameters, i.e. the number of spatial covariates considered, increases with the expected number of data points. Both procedures are compared theoretically, in a simulation study, and in a real data example.