A weighted estimating equation approach for inhomogeneous spatial point processes

Yongtao Guan, Ye Shen

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

We introduce a new estimation method for parametric intensity function models of inhomogeneous spatial point processes based on weighted estimating equations. The weights can incorporate information on both inhomogeneity and dependence of the process. Simulations show that significant efficiency gains can be achieved for non-Poisson processes, compared to the Poisson maximum likelihood estimator. An application to tropical forest data illustrates the use of the proposed method.

Original languageEnglish (US)
Pages (from-to)867-880
Number of pages14
JournalBiometrika
Volume97
Issue number4
DOIs
StatePublished - Dec 2010
Externally publishedYes

Fingerprint

Weighted Estimating Equations
Spatial Point Process
Maximum likelihood
Intensity Function
Inhomogeneity
Maximum Likelihood Estimator
tropical forests
Siméon Denis Poisson
Weights and Measures
methodology
Simulation
Point process
Model

Keywords

  • Inhomogeneous spatial point process
  • Intensity estimation
  • Poisson maximum likelihood
  • Weighted estimating equation

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Mathematics(all)
  • Applied Mathematics
  • Statistics and Probability

Cite this

A weighted estimating equation approach for inhomogeneous spatial point processes. / Guan, Yongtao; Shen, Ye.

In: Biometrika, Vol. 97, No. 4, 12.2010, p. 867-880.

Research output: Contribution to journalArticle

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