Articles |
Estimating functions for inhomogeneous spatial point processes with incomplete covariate data
Department of Mathematical Sciences, Aalborg University, Fredrik Bajersvej 7G, DK-9220 Aalborg, Denmark rw{at}math.aau.dk
Received for publication 1 January 2007. Revision received 1 November 2007.
The R package spatstat provides a very flexible and useful framework for analysing spatial point patterns. A fundamental feature is a procedure for fitting spatial point process models depending on covariates. However, in practice one often faces incomplete observation of the covariates and this leads to parameter estimation error which is difficult to quantify. In this paper, we introduce a Monte Carlo version of the estimating function used in spatstat for fitting inhomogeneous Poisson processes and certain inhomogeneous cluster processes. For this modified estimating function, it is feasible to obtain the asymptotic distribution of the parameter estimators in the case of incomplete covariate information. This allows a study of the loss of efficiency due to the missing covariate data.
Key Words: Asymptotic normality • Cluster process • Estimating function • Experimental design • Inhomogeneous point process • Missing covariate data • Poisson process
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