An estimating function approach to inference for inhomogeneous Neyman-Scott processes

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Abstrakt

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.
Udgivelsesdato: MAR
OriginalsprogEngelsk
TidsskriftBiometrics
Vol/bind63
Udgave nummer1
Sider (fra-til)252-258
Antal sider7
ISSN0006-341X
DOI
StatusUdgivet - 2007

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