A Non-Gaussian Spatial Generalized Linear Latent Variable Model

Citation data:

Journal of Agricultural, Biological, and Environmental Statistics, ISSN: 1085-7117, Vol: 17, Issue: 3, Page: 332-353

Publication Year:
2012
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Repository URL:
http://hdl.handle.net/10754/597349
DOI:
10.1007/s13253-012-0099-5
Author(s):
Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.
Publisher(s):
Springer Nature
Tags:
Mathematics; Environmental Science; Agricultural and Biological Sciences; Decision Sciences; Copula; Factor analysis; Latent variable; Mixture of Gaussians; Multivariate random field; Non-normal; Spatial data
article description
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.