# Regularized estimation for highly multivariate log Gaussian Cox processes

## TL;DR

This paper introduces stable and efficient methods for estimating parameters in complex multivariate log Gaussian Cox processes, enabling better analysis of high-dimensional point pattern data, exemplified by tropical rainforest ecology.

## Contribution

It develops novel numerical algorithms for parameter estimation and model selection in highly multivariate log Gaussian Cox processes, addressing computational challenges.

## Key findings

- Algorithms are numerically stable and efficient.
- Applied successfully to tropical rainforest data.
- Improves modeling of complex multivariate point patterns.

## Abstract

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.