Regularized estimation for highly multivariate log Gaussian Cox processes
Methodology
2019-05-07 v1 Computation
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.
Cite
@article{arxiv.1905.01455,
title = {Regularized estimation for highly multivariate log Gaussian Cox processes},
author = {Achmad Choiruddin and Francisco Cuevas-Pacheco and Jean-François Coeurjolly and Rasmus Waagepetersen},
journal= {arXiv preprint arXiv:1905.01455},
year = {2019}
}