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A Non-Gaussian Spatial Generalized Linear Latent Variable Model

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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.

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Authors and Affiliations

  1. Department of Statistical Science, Duke University, Durham, USA

    Irina Irincheeva

  2. Department of Statistics, Texas A&M University, College Station, USA

    Marc G. Genton

Authors
  1. Irina Irincheeva
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  2. Eva Cantoni
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  3. Marc G. Genton
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Corresponding author

Correspondence to Irina Irincheeva.

Additional information

The research was carried out while the first author was a Ph.D. Student at the University of Geneva.

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Irincheeva, I., Cantoni, E. & Genton, M.G. A Non-Gaussian Spatial Generalized Linear Latent Variable Model. JABES 17, 332–353 (2012). https://doi.org/10.1007/s13253-012-0099-5

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  • DOI: https://doi.org/10.1007/s13253-012-0099-5

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