A fast spectral quasi-likelihood approach for spatial point processes

C. Deng; R. P. Waagepetersen; M. Wang; Y. Guan

doi:10.1016/j.spl.2017.09.016

A fast spectral quasi-likelihood approach for spatial point processes

C. Deng, R. P. Waagepetersen, M. Wang, Y. Guan

Management Science

Research output: Contribution to journal › Article › peer-review

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	https://doi.org/10.1016/j.spl.2017.09.016
State	Published - Feb 2018

Keywords

Estimating function
Quasi-Likelihood
Spatial point process
Spectral approach

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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title = "A fast spectral quasi-likelihood approach for spatial point processes",

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).",

keywords = "Estimating function, Quasi-Likelihood, Spatial point process, Spectral approach",

author = "C. Deng and Waagepetersen, {R. P.} and M. Wang and Y. Guan",

note = "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 .",

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doi = "10.1016/j.spl.2017.09.016",

language = "English (US)",

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journal = "Statistics and Probability Letters",

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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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JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

SN - 0167-7152

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A fast spectral quasi-likelihood approach for spatial point processes

Abstract

Keywords

ASJC Scopus subject areas

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