[Statlist] Next talk: Friday, 02.11.2018, with Haakon Bakka, King Abdullah University of Science and Technology

[Maurer Letizia]

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ETH and University of Zurich

Organisers:

Proff. P. Bühlmann - L. Held - T. Hothorn - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf

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We are glad to announce the following talk:

Friday, 02.11.2018, at 15.15 h  ETH Zurich HG E33.5
with Haakon Bakka, King Abdullah University of Science and Technology

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Title:

Non-stationary Gaussian models with physical barriers<https://www.math.ethz.ch/sfs/news-and-events/research-seminar.html?s=hs18#e_12108>

Abstract:

When modeling spatial data near the coast, we need to consider which assumptions to make on the Gaussian field with respect to the coastline, i.e. what kind of boundary effect to assume. One possibility is to have no boundary effect, modeling both water and land, but with observation and prediction locations only in water, leading to a model with a stationary correlation structure. However, a stationary field smooths over islands and peninsulas, inappropriately assuming that observations on two sides of land are highly correlated. Other approaches in the literature range from simple use of Dirichlet or Neumann boundary conditions, to being quite complex and costly. In this talk I showcase a new approach, the Barrier model, implemented in R-INLA, that is intuitive in the way correlation follows the coastline, and is as easy to set up and do inference with as a stationary field, with computational complexity O(n sqrt(n)). I compare this model to two others, showing significant improvement at reconstructing a test function. A real data application shows that the Barrier model smooths around peninsulas, and that inference is numerically stable. I also detail how the stochastic partial differential equations (SPDE) approach was used to construct the Barrier model.

This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar

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