[Forum SIS] Seminar

[Simone Padoan]

Dear All,
We are glad to announce the following talk

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DEC - Statistics Seminar - Università Bocconi,

Thursday, March 5th, 2015,

Room 3-E4-SR03, Via Roentgen 1 - 3rd floor

Time: 12:30pm

(The complete DEC seminars schedule is available at            
                 http://www.unibocconi.eu/statseminar)
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Speaker

Alan E. Gelfand
(Duke University)


Title

"Spatial data and Gaussian processes: A beautiful marriage"


Abstract


In the past twenty years analysis of spatial data has become increasingly model-based. Full specification of stochastic models for the spatial process being investigated enables full inference and uncertainty assessment regarding the process.
Gaussian processes on subsets of R2 have become a fundamental specification for such modeling, particularly in settings where prediction is a primary goal.
Therefore, focusing on the point-referenced case, we elaborate the substantial range of spatial settings where Gaussian processes have enabled rich and flexible modeling.
We start with the basic geostatistical model, in hierarchical form, moving to generalized spatial regression models, multivariate process models, and spatially varying coefficient models. We will consider the use of Gaussian processes to handle skewed distributions as well as nonparametric distributional models and also the role of Gaussian processes in dimension reduction strategies to accommodate large datasets. Also, we will look at less standard contexts including spatial extremes, spatial directional data, and spatial quantile regression. Modeling details, model fitting, and examples will be provided.

Key words: Asymmetric Laplace process, Extreme value spatial process; projected Gaussian process; skewed Gaussian process; wrapped Gaussian process



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Dr. Simone Padoan, PhD.
Assistant Professor

Department of Decision Sciences
Bocconi University of Milan
via Roentgen, 1 20136 Milano - Italy
tel. +39 02 5836 5368
Email: simone.padoan a unibocconi.it
Webpage: http://faculty.unibocconi.it/simonepadoan
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