Regularized estimation for highly multivariate log Gaussian Cox processes

Achmad Choiruddin, Francisco Andrés Cuevas Pacheco, Jean-Francois Coeurjolly, Rasmus Waagepetersen

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Abstrakt

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.
OriginalsprogEngelsk
TidsskriftStatistics and Computing
Vol/bind30
Udgave nummer3
Sider (fra-til)649–662
Antal sider14
ISSN0960-3174
DOI
StatusUdgivet - 2020

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