TY - JOUR
T1 - A fast spectral quasi-likelihood approach for spatial point processes
AU - Deng, C.
AU - Waagepetersen, R. P.
AU - Wang, M.
AU - Guan, Y.
N1 - Funding Information:
Rasmus Waagepetersen was supported by The Danish Council for Independent Research-Natural Sciences , grant DFF - 7014-00074 “Statistics for point processes in space and beyond”, and by the “Centre for Stochastic Geometry and Advanced Bioimaging”, funded by grant 8721 from the Villum Foundation .
PY - 2018/2
Y1 - 2018/2
N2 - In applications of spatial point processes, it is often of interest to fit a parametric model for the intensity function. For this purpose Guan et al. (2015) recently introduced a quasi-likelihood type estimating function that is optimal in a certain class of first-order estimating functions. However, depending on the choice of certain tuning parameters, the implementation suggested in Guan et al. (2015) can be very demanding both in terms of computing time and memory requirements. Using a novel spectral representation, we construct in this paper an implementation that is computationally much more efficient than the one proposed in Guan et al. (2015).
AB - In applications of spatial point processes, it is often of interest to fit a parametric model for the intensity function. For this purpose Guan et al. (2015) recently introduced a quasi-likelihood type estimating function that is optimal in a certain class of first-order estimating functions. However, depending on the choice of certain tuning parameters, the implementation suggested in Guan et al. (2015) can be very demanding both in terms of computing time and memory requirements. Using a novel spectral representation, we construct in this paper an implementation that is computationally much more efficient than the one proposed in Guan et al. (2015).
KW - Estimating function
KW - Quasi-Likelihood
KW - Spatial point process
KW - Spectral approach
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U2 - 10.1016/j.spl.2017.09.016
DO - 10.1016/j.spl.2017.09.016
M3 - Article
AN - SCOPUS:85033586445
VL - 133
SP - 59
EP - 64
JO - Statistics and Probability Letters
JF - Statistics and Probability Letters
SN - 0167-7152
ER -