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dc.contributor.authorIrincheeva, Irina
dc.contributor.authorCantoni, Eva
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2016-02-25T12:31:17Z
dc.date.available2016-02-25T12:31:17Z
dc.date.issued2012-08-03
dc.identifier.citationIrincheeva I, Cantoni E, Genton MG (2012) A Non-Gaussian Spatial Generalized Linear Latent Variable Model. JABES 17: 332–353. Available: http://dx.doi.org/10.1007/s13253-012-0099-5.
dc.identifier.issn1085-7117
dc.identifier.issn1537-2693
dc.identifier.doi10.1007/s13253-012-0099-5
dc.identifier.urihttp://hdl.handle.net/10754/597349
dc.description.abstractWe consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
dc.description.sponsorshipGenton's research was partially supported by NSF Grant DMS-1007504, and by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
dc.publisherSpringer Nature
dc.subjectCopula
dc.subjectFactor analysis
dc.subjectLatent variable
dc.subjectMixture of Gaussians
dc.subjectMultivariate random field
dc.subjectNon-normal
dc.subjectSpatial data
dc.titleA Non-Gaussian Spatial Generalized Linear Latent Variable Model
dc.typeArticle
dc.identifier.journalJournal of Agricultural, Biological, and Environmental Statistics
dc.contributor.institutionDuke University, Durham, United States
dc.contributor.institutionUniversite de Geneve, Geneve, Switzerland
dc.contributor.institutionTexas A and M University, College Station, United States
kaust.grant.numberKUS-C1-016-04
dc.date.published-online2012-08-03
dc.date.published-print2012-09


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