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Article Dans Une Revue Statistics and Computing Année : 2020

Regularized estimation for highly multivariate log Gaussian Cox processes

Résumé

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.

Dates et versions

hal-03351066 , version 1 (21-09-2021)

Identifiants

Citer

Achmad Choiruddin, Francisco Cuevas-Pacheco, Jean-François Coeurjolly, Rasmus Waagepetersen. Regularized estimation for highly multivariate log Gaussian Cox processes. Statistics and Computing, 2020, 30 (3), pp.649-662. ⟨10.1007/s11222-019-09911-y⟩. ⟨hal-03351066⟩
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