[ABE-L] Seminário DEST/UFMG em 28/04/2023

[Marcos Prates]

Caros,

Na próxima sexta-feira (28 de Abril, às 13:30h) o ciclo de Seminários do
Departamento de Estatística da UFMG terá a apresentação do prof. Bruno
Sansó.

Bruno Sansó is Professor, Department of Statistics, University of
California Santa Cruz.
Sansó's PhD is from Universidad Central de Venezuela, 1992. He is an expert
in Bayesian hierarchical models for space, time and space-time models,
extreme values, computer model emulation and calibration, and point
processes. His work focuses on environmental and climatological
applications.  Sansó was Professor and co-founder of the Department of
Scientific Computing and Statistics, Universidad Simón Bolívar, Venezuela.
In 2001 he joined the University of California Santa Cruz Department of
Applied Mathematics and Statics, being department chair during 2009-2014.
He has supervised many graduate students. One of them
won the Savage Award in 2010.  Sansó's publications have appeared in the
most highly ranked statistical journals, obtaining some prestigious awards,
like the Mitchell Prize in 2009 and 2019. Sansó was Associate Editor of
JSPI and Technometrics. He was Editor in Chief of the journal
Bayesian Analysis.  He has had appointed and elected leadership roles in
the American Statistical Association, the International Environmetrics
Society, The Bernoulli Society and the International Society for Bayesian
Analysis.  Sansó is Elected Member of the International Statistical
Institute, Fellow of the American Statistical Association, and Fellow of
the International Society for Bayesian Analysis.

Título: Non-Gaussian geostatistical models using nearest neighbors
processes.

Resumo:
We present a framework for non-Gaussian spatial processes that encompasses
large distribution families. Spatial dependence for a set of irregularly
scattered locations is described with a mixture of pairwise kernels.
Focusing on the nearest neighbors of a given location, within a reference
set, we obtain a valid spatial process: the nearest neighbor mixture
process (NNMP). We develop conditions to construct general NNMP models with
arbitrary pre-specified marginal distributions. Essentially, NNMPs are
specified by a bi-variate distribution, with suitable marginals, used to
specify the mixture transition kernels. Such distribution can be spatially
varying, to capture non-homogeneous spatial features. The mixture structure
of the model allows for efficient MCMC-based exploration of posterior
distribution of the model parameters, even for relatively large number of
locations. We illustrate the capabilities of NNMPs with observations
corresponding to distributions with different non-Gaussian characteristics:
Long tails; Compact support; Skewness; Discrete values.

O seminário será transmitido ao vivo pelo canal do Youtube "Seminários DEST
- UFMG <https://www.youtube.com/@seminariosdest-ufmg>".

https://www.youtube.com/@seminariosdest-ufmg

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