A Non-Gaussian Spatial Generalized Linear Latent Variable Model

作者：Irincheeva Irina^*; Cantoni Eva; Genton Marc G

来源：Journal of Agricultural, Biological, and Environmental Statistics, 2012, 17(3): 332-353.

DOI：10.1007/s13253-012-0099-5

• Copula
• Factor analysis
• Latent variable
• Mixture of Gaussians
• Multivariate random field
• Non-normal
• Spatial data

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.

• 出版日期2012-9