Two-step estimation for inhomogeneous spatial point processes

Rasmus Waagepetersen, Yongtao Guan

Research output: Contribution to journalArticlepeer-review

101 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)685-702
Number of pages18
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number3
StatePublished - Jun 2009
Externally publishedYes


  • Asymptotic normality
  • Clustering
  • Estimating function
  • Inhomogeneous point process
  • Intensity function
  • K-function
  • Log-Gaussian Cox process
  • Minimum contrast estimation
  • Neyman-Scott point process

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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