Article Dans Une Revue Statistics and Computing Année : 2019

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.

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hal-02119264 , version 1 (03-05-2019)

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Achmad Choiruddin, Francisco Cuevas-Pacheco, Jean-François Coeurjolly, Rasmus Waagepetersen. Regularized estimation for highly multivariate log Gaussian Cox processes. Statistics and Computing, 2019, 30 (3), pp.649-662. ⟨10.1007/s11222-019-09911-y⟩. ⟨hal-02119264⟩
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