An estimating function approach to inference for inhomogeneous Neyman-Scott processes.
This article is concerned with inference for a certain class of inhomogeneous Neyman-Scott point processes depending on spatial covariates. Regression parameter estimates obtained from a simple estimating function are shown to be asymptotically normal when the "mother" intensity for the Neyman-Scott process tends to infinity. Clustering parameter estimates are obtained using minimum contrast estimation based on the K-function. The approach is motivated and illustrated by applications to point pattern data from a tropical rain forest plot.
Waagepetersen
Rasmus Plenge
RP
Institute of Mathematical Sciences, Aalborg University, Fredrik Bajersvej 7G, DK-9220 Aalborg, Denmark. rw@math.aau.dk
D001699:Biometry; D003198:Computer Simulation; D019770:Lauraceae / Q000737:chemistry; D015233:Models, Statistical; D009010:Monte Carlo Method; D016012:Poisson Distribution