An Estimating Function Approach to Inference for Inhomogeneous Neyman–Scott Processes

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Abstract

Summary 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.

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