TY - JOUR
T1 - Regularized estimation for highly multivariate log Gaussian Cox processes
AU - Choiruddin, Achmad
AU - Pacheco, Francisco Andrés Cuevas
AU - Coeurjolly, Jean-Francois
AU - Waagepetersen, Rasmus
PY - 2020
Y1 - 2020
N2 - Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for a class of multivariate log Gaussian Cox processes. The methodology is applied to a highly multivariate point pattern data set from tropical rain forest ecology.
AB - Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for a class of multivariate log Gaussian Cox processes. The methodology is applied to a highly multivariate point pattern data set from tropical rain forest ecology.
KW - Cross-pair correlation
KW - Elastic net
KW - LASSO
KW - Log Gaussian Cox process
KW - Multivariate point process
KW - Proximal Newton method
UR - http://www.scopus.com/inward/record.url?scp=85075242510&partnerID=8YFLogxK
U2 - 10.1007/s11222-019-09911-y
DO - 10.1007/s11222-019-09911-y
M3 - Journal article
SN - 0960-3174
VL - 30
SP - 649
EP - 662
JO - Statistics and Computing
JF - Statistics and Computing
IS - 3
ER -