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 | |
| State | Published - Dec 2010 |
| Externally published | Yes |
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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 journal › Article
}
TY - JOUR
T1 - A weighted estimating equation approach for inhomogeneous spatial point processes
AU - Guan, Yongtao
AU - Shen, Ye
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
UR - http://www.scopus.com/inward/record.url?scp=78651267234&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78651267234&partnerID=8YFLogxK
U2 - 10.1093/biomet/asq043
DO - 10.1093/biomet/asq043
M3 - Article
AN - SCOPUS:78651267234
VL - 97
SP - 867
EP - 880
JO - Biometrika
JF - Biometrika
SN - 0006-3444
IS - 4
ER -