TY - JOUR
T1 - A marked point process perspective in fitting spatial point process models
AU - Guan, Yongtao
N1 - Funding Information:
The author thanks the Editor, the Associate Editor and a referee for their helpful comments that have improved the paper. This research was partially supported by National Science Foundation Grant DMS-0603673.
PY - 2008/7/1
Y1 - 2008/7/1
N2 - This paper discusses a new perspective in fitting spatial point process models. Specifically the spatial point process of interest is treated as a marked point process where at each observed event x a stochastic process M (x ; t), 0 < t < r, is defined. Each mark process M (x ; t) is compared with its expected value, say F (t ; θ), to produce a discrepancy measure at x, where θ is a set of unknown parameters. All individual discrepancy measures are combined to define an overall measure which will then be minimized to estimate the unknown parameters. The proposed approach can be easily applied to data with sample size commonly encountered in practice. Simulations and an application to a real data example demonstrate the efficacy of the proposed approach.
AB - This paper discusses a new perspective in fitting spatial point process models. Specifically the spatial point process of interest is treated as a marked point process where at each observed event x a stochastic process M (x ; t), 0 < t < r, is defined. Each mark process M (x ; t) is compared with its expected value, say F (t ; θ), to produce a discrepancy measure at x, where θ is a set of unknown parameters. All individual discrepancy measures are combined to define an overall measure which will then be minimized to estimate the unknown parameters. The proposed approach can be easily applied to data with sample size commonly encountered in practice. Simulations and an application to a real data example demonstrate the efficacy of the proposed approach.
KW - K-function
KW - Marked point process
KW - Spatial point process
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U2 - 10.1016/j.jspi.2007.09.008
DO - 10.1016/j.jspi.2007.09.008
M3 - Article
AN - SCOPUS:40949131814
VL - 138
SP - 2143
EP - 2153
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
SN - 0378-3758
IS - 7
ER -