  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.
