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 language | English (US) |
|---|---|
| Pages (from-to) | 1502-1512 |
| Number of pages | 11 |
| Journal | Journal of the American Statistical Association |
| Volume | 101 |
| Issue number | 476 |
| DOIs | |
| State | Published - Dec 2006 |
| Externally published | Yes |
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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 journal › Article
}
TY - JOUR
T1 - A composite likelihood approach in fitting spatial point process models
AU - Guan, Yongtao
PY - 2006/12
Y1 - 2006/12
N2 - 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.
AB - 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.
KW - Composite likelihood
KW - Spatial point process
UR - http://www.scopus.com/inward/record.url?scp=33846092776&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33846092776&partnerID=8YFLogxK
U2 - 10.1198/016214506000000500
DO - 10.1198/016214506000000500
M3 - Article
VL - 101
SP - 1502
EP - 1512
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
IS - 476
ER -