In the present paper we consider the problem of fitting parametric spatial Cox point process models. We concentrate on the moment estimation methods based on the second order characteristics of the point process in question. These methods represent a simulation-free faster-to-compute alternative to the computationally intense maximum likelihood estimation. We give an overview of the available methods, discuss their properties and applicability. Further we present results of a simulation study in which performance of these estimating methods was compared for planar point processes with different types and strength of clustering and inter-point interactions.
moment estimation methods, spatial Cox point process, parametric inference