Noël Cressie - Ohio State University Bande son disponible au format mp3 Durée : 10 mn

Modeling space-time data often relies on parametric covariance models and various assumptions such as full symmetry and separability. These assumptions are important because they simplify the structure of the model and its inference, and ease the possibly extensive computational burden associated with spacetime data sets. We review various space-time covariance models and propose a unified framework for testing a variety of assumptions commonly made for covariance functions of stationary...

In geostatistics, a common problem is to predict a spatial exceedance and its exceedance region. This is scientifically important since unusual events tend to strongly impact the environment. Here, we use classes of loss functions based on image metrics (e.g., Baddeley's loss function) to predict the spatial-exceedance region. We then propose a joint loss to predict a spatial quantile and its exceedance region. The optimal predictor is obtained by minimizing the posterior expected loss...

Marc Genton - University of Geneva Bande son disponible au format mp3 Durée : 5 mn

We develop contrasting spatio-temporal models for two weather variables: solar radiation and rainfall. For solar radiation the aim is to assess the performance of area networks of photo-voltaic cells. Although radiation measured at a sufficiently fine temporal scale has a bimodal marginal distribution (Glasbey, 2001), averages of 10-minute or longer duration can be transformed to be approximately Gaussian, and we fit a spatio-temporal auto-regressive moving average (STARMA) process...

Chris Glasbey - Biomathematics and Statistics Scotland Bande son disponible au format mp3 Durée : 14 mn

Soil moisture provides the physical link between soil, climate and vegetation. It increases via the infiltration of rainfall and decreases through evapotranspiration, run-off and leakage, all these effects being dependent on the existing soil moisture level. During wet periods, soil moisture tends largely to be driven by the topography, while evapotranspiration has more influence in dry periods. In this talk, I will describe models for soil moisture dynamics in which marked Poisson processes...

Valérie Isham - University College, London Bande son disponible au format mp3 Durée : 8 mn

The fields of geographical epidemiology and public health surveillance have benefited from combined advances in hierarchical model building and in geographical information systems. Exploring and characterising a variety of spatial patterns of diseases at a fine geographical resolution has become possible (Banerjee, Carlin and Gelfand 2004). Insight into the sensitivity of the resulting inference to the choice of the structure of the different components of the hierarchical model has been...

Sylvia Richardson - Imperial College, London Bande son disponible au format mp3 Durée : 13 mn

Gaussian models are frequently used within spatial statistics and often as a latent Gaussian model is hierachical formulations. The devellopment of Markov chain Monte Carlo methods also allow for spatial analysis of non-Gaussian observations like spatial count and survial data. Although MCMC is doable it is not without practical hassle like long computing time and slow convergence.In this talk, I will present an alternative strategy, for which the aim is to approximate all posterior...

Havard Rue - Norwegian University of Science and Technology Bande son disponible au format mp3 Durée : 18 mn

Bande son disponible au format mp3 Durée : 19 mn