Quasi-likelihood for Spatial Point Processes

Fitting regression models for intensity functions of spatial point processes is of great interest in ecological and epidemiological studies of association between spatially referenced events and geographical or environmental covariates. When Cox or cluster process models are used to accommodate clustering not accounted for by the available covariates, likelihood based inference becomes computationally cumbersome due to the complicated nature of the likelihood function and the associated score function. It is therefore of interest to consider alternative more easily computable estimating functions. We derive the optimal estimating function in a class of first-order estimating functions. The optimal estimating function depends on the solution of a certain Fredholm integral equation which in practice is solved numerically. The approximate solution is equivalent to a quasi-likelihood for binary spatial data and we therefore use the term quasi-likelihood for our optimal estimating function approach. We demonstrate in a simulation study and a data example that our quasi-likelihood method for spatial point processes is both statistically and computationally efficient

Orthogonal series estimation of the pair correlation function of a spatial point process

The pair correlation function is a fundamental spatial point process characteristic that, given the intensity function, determines second order moments of the point process. Non-parametric estimation of the pair correlation function is a typical initial step of a statistical analysis of a spatial point pattern. Kernel estimators are popular but especially for clustered point patterns suffer from bias for small spatial lags. In this paper we introduce a new orthogonal series estimator. The new estimator is consistent and asymptotically normal according to our theoretical and simulation results. Our simulations further show that the new estimator can outperform the kernel estimators in particular for Poisson and clustered point processes

A tutorial on Palm distributions for spatial point processes

This tutorial provides an introduction to Palm distributions for spatial point processes. Initially, in the context of finite point processes , we give an explicit definition of Palm distributions in terms of their density functions. Then we review Palm distributions in the general case. Finally we discuss some examples of Palm distributions for specific models and some applications

Second-order variational equations for spatial point processes with a view to pair correlation function estimation

Second-order variational type equations for spatial point processes are established. In case of log linear parametric models for pair correlation functions, it is demonstrated that the variational equations can be applied to construct estimating equations with closed form solutions for the parameter estimates. This result is used to fit orthogonal series expansions of log pair correlation functions of general form

Palm distributions for log Gaussian Cox processes

This paper establishes a remarkable result regarding Palmdistributions for a log Gaussian Cox process: the reduced Palmdistribution for a log Gaussian Cox process is itself a log Gaussian Coxprocess which only differs from the original log Gaussian Cox processin the intensity function. This new result is used to study functionalsummaries for log Gaussian Cox processes

Regularized estimation for highly multivariate log Gaussian Cox processes

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

Seed dispersal, microsites or competition-what drives gap regeneration in an old-growth forest? An application of spatial point process modelling

The spatial structure of trees is a template for forest dynamics and the outcome of a variety of processes in ecosystems. Identifying the contribution and magnitude of the different drivers is an age-old task in plant ecology. Recently, the modelling of a spatial point process was used to identify factors driving the spatial distribution of trees at stand scales. Processes driving the coexistence of trees, however, frequently unfold within gaps and questions on the role of resource heterogeneity within-gaps have become central issues in community ecology. We tested the applicability of a spatial point process modelling approach for quantifying the effects of seed dispersal, within gap light environment, microsite heterogeneity, and competition on the generation of within gap spatial structure of small tree seedlings in a temperate, old growth, mixed-species forest. By fitting a non-homogeneous Neyman–Scott point process model, we could disentangle the role of seed dispersal from niche partitioning for within gap tree establishment and did not detect seed densities as a factor explaining the clustering of small trees. We found only a very weak indication for partitioning of within gap light among the three species and detected a clear niche segregation of Picea abies (L.) Karst. on nurse logs. The other two dominating species, Abies alba Mill. and Fagus sylvatica L., did not show signs of within gap segregation