Abstract
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).
| Original language | English (US) |
|---|---|
| Pages (from-to) | 59-64 |
| Number of pages | 6 |
| Journal | Statistics and Probability Letters |
| Volume | 133 |
| DOIs | |
| State | Published - Feb 1 2018 |
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Keywords
- Estimating function
- Quasi-Likelihood
- Spatial point process
- Spectral approach
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
A fast spectral quasi-likelihood approach for spatial point processes. / Deng, C.; Waagepetersen, R. P.; Wang, M.; Guan, Yongtao.
In: Statistics and Probability Letters, Vol. 133, 01.02.2018, p. 59-64.Research output: Contribution to journal › Article
}
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, Yongtao
PY - 2018/2/1
Y1 - 2018/2/1
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
UR - http://www.scopus.com/inward/record.url?scp=85033586445&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85033586445&partnerID=8YFLogxK
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 -