A weighted estimating equation approach for inhomogeneous spatial point processes

Yongtao Guan; Ye Shen

doi:10.1093/biomet/asq043

A weighted estimating equation approach for inhomogeneous spatial point processes

Yongtao Guan, Ye Shen

Research output: Contribution to journal › Article › peer-review

22 Scopus citations

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 language	English (US)
Pages (from-to)	867-880
Number of pages	14
Journal	Biometrika
Volume	97
Issue number	4
DOIs	https://doi.org/10.1093/biomet/asq043
State	Published - Dec 1 2010
Externally published	Yes

Keywords

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

ASJC Scopus subject areas

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

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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.",

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AU - Shen, Ye

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AB - 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.

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KW - Intensity estimation

KW - Poisson maximum likelihood

KW - Weighted estimating equation

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A weighted estimating equation approach for inhomogeneous spatial point processes

Abstract

Keywords

ASJC Scopus subject areas

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