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 |
Keywords
- Estimating function
- Quasi-Likelihood
- Spatial point process
- Spectral approach
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Fingerprint Dive into the research topics of 'A fast spectral quasi-likelihood approach for spatial point processes'. Together they form a unique fingerprint.
Cite this
Deng, C., Waagepetersen, R. P., Wang, M., & Guan, Y. (2018). A fast spectral quasi-likelihood approach for spatial point processes. Statistics and Probability Letters, 133, 59-64. https://doi.org/10.1016/j.spl.2017.09.016