Analysis of multispecies point patterns by usingmultivariate log-Gaussian Cox processes
View/ Open
Impact
Scholar |
Other documents of the author: Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah; Mateu, Jorge
Metadata
Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
Analysis of multispecies point patterns by usingmultivariate log-Gaussian Cox processesDate
2016Publisher
Royal Statistical Society; WileyISSN
0035-9254; 1467-9876Bibliographic citation
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.Type
info:eu-repo/semantics/articlePublisher version
http://onlinelibrary.wiley.com/enhanced/doi/10.1111/rssc.12108/Version
info:eu-repo/semantics/sumittedVersionSubject
Abstract
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. [-]
Is part of
Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, vol. 65, no 1Rights
© Royal Statistical Society
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
This item appears in the folowing collection(s)
- MAT_Articles [755]