Two-step estimation for inhomogeneous spatial point processes

Description

This project 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 using a Poisson likelihood score estimating function and in a secondstep 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 rain forests.
StatusFinished
Effective start/end date01/01/200801/09/2010

Funding

  • <ingen navn>

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Spatial Point Process
Mixing Conditions
Intensity Function
Estimating Function
Score Function
Asymptotic Normality
Parameter Estimation
Likelihood
Regression Model
Siméon Denis Poisson
Regression
Clustering
Estimate