Analysis of multispecies point patterns by usingmultivariate log-Gaussian Cox processes
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Altres documents de l'autoria: Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah; Mateu, Jorge
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Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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Analysis of multispecies point patterns by usingmultivariate log-Gaussian Cox processesData de publicació
2016Editor
Royal Statistical Society; WileyISSN
0035-9254; 1467-9876Cita bibliogràfica
WAAGEPETERSEN, Rasmus, et al. Analysis of multispecies point patterns by using multivariate log‐Gaussian Cox processes. Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, vol. 65, no 1, p. 77-96.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
http://onlinelibrary.wiley.com/enhanced/doi/10.1111/rssc.12108/Versió
info:eu-repo/semantics/sumittedVersionParaules clau / Matèries
Resum
Multivariate log-Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far been applied in bivariate cases only. We move beyond the bivariate case to model multispecies point ... [+]
Multivariate log-Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far been applied in bivariate cases only. We move beyond the bivariate case to model multispecies point patterns of tree locations. In particular we address the problems of identifying parsimonious models and of extracting biologically relevant information from the models fitted. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows a decomposition of variance that can be used to quantify to what extent the spatial variation of a species is governed by common or species-specific factors. Cross-validation is used to select the number of common latent fields to obtain a suitable trade-off between parsimony and fit of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields. [-]
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Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, vol. 65, no 1Drets d'accés
© Royal Statistical Society
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