TY - JOUR
T1 - A composite likelihood approach in fitting spatial point process models
AU - Guan, Yongtao
N1 - Funding Information:
Yongtao Guan is Assistant Professor, Division of Biostatistics, Yale University, New Haven, CT 06520-8034 (E-mail: yongtao.guan@yale.edu). The author thanks the editor, the associate editor, and two referees for their helpful comments which have greatly improved this manuscript both in content and exposition. The work was supported in part by NSF grant DMS-06-03673 and a summer research grant from the School of Business at University of Miami.
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
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U2 - 10.1198/016214506000000500
DO - 10.1198/016214506000000500
M3 - Article
AN - SCOPUS:33846092776
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 -