TY - JOUR
T1 - Quasi-likelihood for multivariate spatial point processes with semiparametric intensity functions
AU - Chu, Tingjin
AU - Guan, Yongtao
AU - Waagepetersen, Rasmus
AU - Xu, Ganggang
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/8
Y1 - 2022/8
N2 - We propose a new estimation method to fit a semiparametric intensity function model to multivariate spatial point processes. Our approach is based on the so-called quasi-likelihood that can produce more efficient estimators by accounting for both between- and within-process correlations. To be more specific, we derive the optimal estimating function in a class of first-order estimating functions, where the optimal estimating function depends on the solution to a system of integral equations. We propose a computationally fast approach to obtain an approximate solution to the integral equation, and the resulting estimation approach is therefore computationally efficient. We demonstrate the efficacy of the proposed approach through both simulations and a real application.
AB - We propose a new estimation method to fit a semiparametric intensity function model to multivariate spatial point processes. Our approach is based on the so-called quasi-likelihood that can produce more efficient estimators by accounting for both between- and within-process correlations. To be more specific, we derive the optimal estimating function in a class of first-order estimating functions, where the optimal estimating function depends on the solution to a system of integral equations. We propose a computationally fast approach to obtain an approximate solution to the integral equation, and the resulting estimation approach is therefore computationally efficient. We demonstrate the efficacy of the proposed approach through both simulations and a real application.
KW - Multivariate point process
KW - Quasi-likelihood
KW - Semiparametric intensity function
UR - http://www.scopus.com/inward/record.url?scp=85124471488&partnerID=8YFLogxK
U2 - 10.1016/j.spasta.2022.100605
DO - 10.1016/j.spasta.2022.100605
M3 - Journal article
AN - SCOPUS:85124471488
SN - 2211-6753
VL - 50
JO - Spatial Statistics
JF - Spatial Statistics
M1 - 100605
ER -