Dynamical non-Gaussian modelling of spatial processes. Issue 1 (13th February 2023)

• Record Type: Journal Article
• Authors: Fonseca, Thaís C O; Lobo, Viviana G R; Schmidt, Alexandra M
• Is Part Of: Journal of the Royal Statistical Society. Volume 72:Issue 1(2023)
• Page Start: 76
• Page End: 103
• Publication Date: 2023-02-13
• Subjects: Bayesian inference -- forward filtering backward sampling -- heavy-tailed process -- nonconstant variance; Statistics -- Periodicals; 519.5
• DOI: 10.1093/jrsssc/qlac007
• Languages: English
• ISSNs: 0035-9254

Abstract

Abstract: Environmental data are often assumed to follow a spatio-temporal Gaussian process, possibly after transformation. However, heterogeneity might have a pattern not accommodated by transformation and modelling the variance laws is an appealing alternative. This work extends the multivariate dynamic Gaussian model by defining the process as a scale mixture with the scale depending on covariates. State-space equations define the temporal dynamics, resulting in feasible inference and prediction. Various simulations studies show that the parameters are identifiable and our proposal recovers simpler structures. The analyses of temperature and ozone illustrate the improvement in quantifying the uncertainty of predictions.