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A composite likelihood approach in fitting spatial point process models. (English) Zbl 1171.62348
Summary: 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.

MSC:
62M30 Inference from spatial processes
62M09 Non-Markovian processes: estimation
62F12 Asymptotic properties of parametric estimators
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