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Two-step estimation for inhomogeneous spatial point processes
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DOI：10.1111/j.1467-9868.2008.00702.x.png)
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Abstract
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The paper is concerned with parameter estimation for inhomogeneous spatial point processes with a regression model for the intensity function and tractable second-order properties (K-function). Regression parameters are estimated by using a Poisson likelihood score estimating function and in the second step minimum contrast estimation is applied for the residual clustering parameters. Asymptotic normality of parameter estimates is established under certain mixing conditions and we exemplify how the results may be applied in ecological studies of rainforests.
Keywords:
Asymptotic normality
Clustering
Estimating function
Inhomogeneous point process
Intensity function
K-function
Log-Gaussian Cox process
Minimum contrast estimation
Neyman-Scott point process
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