Título: Dynamical non-Gaussian modelling of spatial processes

Palestrante: Viviana das Graças Ribeiro Lobo (DME - UFRJ)
Data: 03/03/2021
Horario: 15:30h
Local: Transmissão online

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Resumo: Spatio-temporal processes in environmental applications are often assumed to follow Gaussian models, under or not particular transformations. However, heterogeneity in space and time may have patterns that are not accommodated by transforming the data in question. In such scenario, modelling the variance is paramount. The methodology presented in this paper adds flexibility to the usual Dynamical Gaussian model by defining the studied process as a scale mixture between a Gaussian process and Log-Gaussian one. The scale is represented by a process varying smoothly over space and time. State-space equations drive the dynamics over time for both response and variance processes resulting in a more computationally efficient estimation and prediction. Two applications are presented. The first one models the maximum temperature in the Spanish Basque Country and the following one models ozone levels in the UK dataset. They illustrate the effectiveness of our proposal in modelling varying variances over both time and space.Jointly with: Thais C. O. Fonseca (DME/UFRJ, Brazil) and Alexandra M. Schmidt (McGill University, Canada).

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