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

25 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 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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10.1093/biomet/asq043

Cite this

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title = "A weighted estimating equation approach for inhomogeneous spatial point processes",

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.",

keywords = "Inhomogeneous spatial point process, Intensity estimation, Poisson maximum likelihood, Weighted estimating equation",

author = "Yongtao Guan and Ye Shen",

note = "Funding Information: We thank the editor, the associate editor and two referees for their valuable comments. Yongtao Guan{\textquoteright}s research was supported by the National Science Foundation, U.S.A. The Barro Colorado Island forest dynamics research project was supported by the National Science Foundation, U.S.A., the Center for Tropical Forest Science, the Smithsonian Tropical Research Institute, the John D. and Catherine T. MacArthur Foundation, the Mellon Foundation, the Celera Foundation and numerous private individuals, and through the hard work of over 100 people from ten countries over the past two decades.",

year = "2010",

month = dec,

doi = "10.1093/biomet/asq043",

language = "English (US)",

volume = "97",

pages = "867--880",

journal = "Biometrika",

issn = "0006-3444",

publisher = "Oxford University Press",

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T1 - A weighted estimating equation approach for inhomogeneous spatial point processes

AU - Guan, Yongtao

AU - Shen, Ye

N1 - Funding Information: We thank the editor, the associate editor and two referees for their valuable comments. Yongtao Guan’s research was supported by the National Science Foundation, U.S.A. The Barro Colorado Island forest dynamics research project was supported by the National Science Foundation, U.S.A., the Center for Tropical Forest Science, the Smithsonian Tropical Research Institute, the John D. and Catherine T. MacArthur Foundation, the Mellon Foundation, the Celera Foundation and numerous private individuals, and through the hard work of over 100 people from ten countries over the past two decades.

PY - 2010/12

Y1 - 2010/12

N2 - 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.

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.

KW - Inhomogeneous spatial point process

KW - Intensity estimation

KW - Poisson maximum likelihood

KW - Weighted estimating equation

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JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 4

ER -

A weighted estimating equation approach for inhomogeneous spatial point processes

Abstract

Keywords

ASJC Scopus subject areas

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