A Non-Gaussian Spatial Generalized Linear Latent Variable Model

Handle URI:
http://hdl.handle.net/10754/597349
Title:
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
Authors:
Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.
Abstract:
We 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.
Citation:
Irincheeva 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.
Publisher:
Springer Nature
Journal:
Journal of Agricultural, Biological, and Environmental Statistics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
3-Aug-2012
DOI:
10.1007/s13253-012-0099-5
Type:
Article
ISSN:
1085-7117; 1537-2693
Sponsors:
Genton'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).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorIrincheeva, Irinaen
dc.contributor.authorCantoni, Evaen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2016-02-25T12:31:17Zen
dc.date.available2016-02-25T12:31:17Zen
dc.date.issued2012-08-03en
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.en
dc.identifier.issn1085-7117en
dc.identifier.issn1537-2693en
dc.identifier.doi10.1007/s13253-012-0099-5en
dc.identifier.urihttp://hdl.handle.net/10754/597349en
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.en
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).en
dc.publisherSpringer Natureen
dc.subjectCopulaen
dc.subjectFactor analysisen
dc.subjectLatent variableen
dc.subjectMixture of Gaussiansen
dc.subjectMultivariate random fielden
dc.subjectNon-normalen
dc.subjectSpatial dataen
dc.titleA Non-Gaussian Spatial Generalized Linear Latent Variable Modelen
dc.typeArticleen
dc.identifier.journalJournal of Agricultural, Biological, and Environmental Statisticsen
dc.contributor.institutionDuke University, Durham, United Statesen
dc.contributor.institutionUniversite de Geneve, Geneve, Switzerlanden
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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