A composite likelihood approach in fitting spatial point process models

Research output: Contribution to journalArticle

48 Citations (Scopus)

Abstract

We propose a new likelihood-based approach in fitting spatial point process models. A composite likelihood is first formed by adding some pairwise composite likelihood functions that are defined in terms of the second-order intensity function of the underlying process, and then used for estimating the unknown parameters. The estimation procedure is computationally simple and yields consistent and asymptotically normal estimators under some mild conditions. We demonstrate through a simulation study and applications to two real data examples that the proposed approach may lead to improved estimations compared with the commonly used "minimum contrast estimation" approach.

Original languageEnglish (US)
Pages (from-to)1502-1512
Number of pages11
JournalJournal of the American Statistical Association
Volume101
Issue number476
DOIs
StatePublished - Dec 2006
Externally publishedYes

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Composite Likelihood
Spatial Point Process
Process Model
Pairwise Likelihood
Composite function
Intensity Function
Likelihood Function
Unknown Parameters
Likelihood
Simulation Study
Estimator
Demonstrate
Process model
Point process

Keywords

  • Composite likelihood
  • Spatial point process

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

Cite this

A composite likelihood approach in fitting spatial point process models. / Guan, Yongtao.

In: Journal of the American Statistical Association, Vol. 101, No. 476, 12.2006, p. 1502-1512.

Research output: Contribution to journalArticle

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