Two-step estimation for inhomogeneous spatial point processes

Rasmus Waagepetersen, Yongtao Guan

Research output: Contribution to journalArticle

83 Citations (Scopus)

Abstract

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
Volume71
Issue number3
DOIs
StatePublished - Jun 2009
Externally publishedYes

Fingerprint

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

Keywords

  • 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, Probability and Uncertainty
  • Statistics and Probability

Cite this

Two-step estimation for inhomogeneous spatial point processes. / Waagepetersen, Rasmus; Guan, Yongtao.

In: Journal of the Royal Statistical Society. Series B: Statistical Methodology, Vol. 71, No. 3, 06.2009, p. 685-702.

Research output: Contribution to journalArticle

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