Two-step estimation for inhomogeneous spatial point processes

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 sec...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2009-06, Vol.71 (3), p.685-702
Hauptverfasser:	Waagepetersen, Rasmus, Guan, Yongtao
Format:	Artikel
Sprache:	eng
Schlagworte:	Asymptotic normality Cluster analysis Clustering Covariance Covariance matrices Ecological studies Eigenvalues Environmental impact studies Estimating function Estimating techniques Estimation Exact sciences and technology General topics Inference from stochastic processes time series analysis Inhomogeneous point process Intensity function K-function Log-Gaussian Cox process Mathematical functions Mathematical independent variables Mathematical models Mathematics Minimum contrast estimation Neyman-Scott point process Parameter estimation Parametric inference Point estimators Poisson distribution Poisson process Probability and statistics Probability theory and stochastic processes Rain forest Rainforests Regression analysis Sciences and techniques of general use Spatial points Statistical methods Statistics Stochastic processes Studies Tropical forests Tropical rain forests
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Beschreibung
Zusammenfassung:	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.
ISSN:	1369-7412 1467-9868
DOI:	10.1111/j.1467-9868.2008.00702.x