A Non-Gaussian Spatial Generalized Linear Latent Variable Model

Type
Article

Authors
Irincheeva, Irina
Cantoni, Eva
Genton, Marc G.

KAUST Grant Number
KUS-C1-016-04

Online Publication Date
2012-08-03

Print Publication Date
2012-09

Date
2012-08-03

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.

Acknowledgements
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).

Publisher
Springer Nature

Journal
Journal of Agricultural, Biological, and Environmental Statistics

DOI
10.1007/s13253-012-0099-5

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