# Regularized estimation for highly multivariate log Gaussian Cox processes

arXiv: https://arxiv.org/abs/1905.01455 · HTML: https://mlsanity.com/paper/1905.01455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2019.

## Built on by (most cited first, 1 of 1)

- [Robust variable selection for spatial point processes observed with noise](https://mlsanity.com/paper/2510.25550/) (2025)

## Abstract (arXiv)

Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for a class of multivariate log Gaussian Cox processes. The methodology is applied to a highly multivariate point pattern data set from tropical rain forest ecology.