**06/03/09 - 15h** Ciclo de Palestras Pós Graduação em Estatística UFRJ

["Alexandra M. Schmidt" <alex@im.ufrj.br>]

Caros, 

Estaremos iniciando o Ciclo de Palestras do Programa de Pós-Graduação em
Estatística do IM-UFRJ, excepcionalmente, nesta 6a feira, 06/03/09, as 15h com
palestras de dois grandes estatísticos da atualidade, Professor Anthony
Davison e Professor Peter Green.

Os titulos e resumos seguem abaixo. Contamos com a presenca de voces.

Acompanhe a atualizacao do programa do nosso ciclo de palestras  no sitio
www.dme.ufrj.br opcao Atividades subopcao Ciclo de Palestras.

Atenciosamente,

Alexandra

Ps.: Desculpem-me pela eventual duplicação da postagem desta mensagem.



Palestrante: Anthony Davison (Lausanne) 
Título: Geostatistics of extremes
Resumo
Climatic change is forecast to change the frequency and sizes of extreme
events such as major storms, heatwaves and the like, and the effects on human
mortality, health and infrastructure are starting to become of major concern
to public health authorities, engineers, and other planners. Predicting the
possible impacts of such events necessarily entails extrapolation outside the
range of the available data, and the usual basis for this is the statistics of
extremes and its underlying probability models. Analysis of extreme events for
single series of data is now well-established and used in a variety of
disciplines, from hydrology through metallurgy to finance and insurance, but
the corresponding theory for events in space is underdeveloped.  After some
motivating material, this talk will describe the basic probabilistic theory of
extremes, and then will outline how it may be extended to the spatial context,
before turning to more statistical matters such as fitting of appropriate
models to data and their use for prediction of future events.


Palestrante: Peter J. Green (Bristol) 
Título: Bayesian model-based clustering procedures and application to gene
expression profiles
Resumo
We present a general framework for Bayesian model-based clustering, in which
subset labels are exchangeable, and items are also exchangeable, possibly up
to covariate effects. It is rich enough to encompass a variety of existing
procedures, including some recently discussed methodologies involving
stochastic search or hierarchical clustering, but more importantly allows the
formulation of clustering procedures that are optimal with respect to a
specified loss function. Our focus is on loss functions based on pairwise
coincidences, that is, whether pairs of items are clustered into the same
subset or not.
We go on to discuss a Bayesian mixture model that allows us to express a gene
expression profile across different experimental conditions as a linear
combination of covariates characterising those conditions, plus error. In a
standard Bayesian nonparametric formulation, the regression coefficients of
the linear combination and the error precisions would jointly follow a
Dirichlet process (DP). In this set-up the clusters generated by the process
are a priori exchangeable. However in the gene expression context, it commonly
occurs that some genes are not influenced by the covariates, but fall into a
`background' class. This calls for an extension to the DP model generating a
background cluster that is not exchangeable with the others.
This is joint work with Dr John Lau, now at University of Western Australia. 

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Alexandra Mello Schmidt, PhD
Professora Adjunta
Instituto de Matemática - UFRJ
Departamento de Métodos Estatísticos
Caixa Postal 68530 Rio de Janeiro - RJ 
CEP:21.945-970 Brasil
Tel: 0055 21 2562 7505 Ramal (Extension) 204
Fax: 0055 21 2562 7374

http://www.dme.ufrj.br/~alex
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There is no time to lose. Cash your dreams before they slip away. 
Lose your dreams and you lose your mind.