[ABE-L] Ciclo de Seminários do PPGE/2026.1

['Francisco Cysneiros' via abe-l@ime.usp.br]

Corrigindo
27 de maio

Francisco José A. Cysneiros
Full professor
Departament of Statistics - UFPE
Co-Director of Department of Statistics, UFPE 2005-2009
Co-Director of Graduate Studies, Statistics, UFPE 2009-2013
Director of Graduate Studies, Statistics, UFPE 2013-2017
Co-Director of Graduate Studies, Statistics, UFPE 2026-2028
Editor in Chief of Brazilian Journal of Probability and Statistics
My URLs:
www.de.ufpe.br/~cysneiros  <http://www.de.ufpe.br/~cysneiros>
http://150.161.44.6/cysneiros

Lattes:

*ResearchedID:*
*Publons:*
http://lattes.cnpq.br/1313497098151734
http://www.researcherid.com/rid/G-6333-2012
https://publons.com/a/1540091
ORCID: http://orcid.org/0000-0001-6757-6969


Description: Enter a Description
<https://www.researcherid.com/ProfileGet.action?focus=description>


Em seg., 25 de mai. de 2026, 12:35, Francisco Cysneiros <
cysneiros em de.ufpe.br> escreveu:

> Ciclo de Seminários do PPGE/2026.1
>
> Temos o prazer de anunciar que, no dia  27 de abril de 2026,
> o seminário será ministrado pela  Profa. Dra. Kelly Cristina Mota
> Gonçalves DME/UFRJ
>
>
> Detalhes da palestra:
>
>
> seminário de Pós-Graduação
> Quarta-feira, 27 de maio · 4:00 – 6:00pm
> Fuso horário: America/Recife
> Como participar do Google Meet
> Link da videochamada: https://meet.google.com/rag-disk-btz
> Ou disque: ‪(BR) +55 19 4560-9802‬ PIN: ‪598 785 307‬#
> Outros números de telefone:
> https://tel.meet/rag-disk-btz?pin=9248224913001
>
> Título:  Bayesian Quantile Regression for Complex Data Structures
> Palestrante: Kelly Cristina Mota Gonçalves DME/UFRJ
>
> Resumo:
> Abstract: Quantile regression provides a flexible framework for modeling
> complex data structures, offering a more comprehensive characterization of
> conditional distributions than traditional mean regression approaches. By
> focusing on different parts of the response distribution, quantile
> regression is naturally robust to heteroscedasticity, outliers, and
> departures from normality frequently observed in real-world applications.
> In the Bayesian setting, inference is commonly based on likelihood
> formulations derived from the asymmetric Laplace distribution and its
> extensions, whose location–scale mixture representations facilitate
> posterior computation. This talk presents Bayesian quantile regression
> approaches for complex data settings, emphasizing recent methodological
> developments for time-varying and spatial models, as well as applications
> involving complex survey data. Practical issues related to estimation,
> computation, and interpretation will also be discussed.
> --
> Francisco José A. Cysneiros
> Full professor
> Departament of Statistics - UFPE
> Co-Director of Department of Statistics, UFPE 2005-2009
> Co-Director of Graduate Studies, Statistics, UFPE 2009-2013
> Director of Graduate Studies, Statistics, UFPE 2013-2017
> Co-Director of Graduate Studies, Statistics, UFPE 2026-2028
> Editor in Chief of Brazilian Journal of Probability and Statistics
> My URLs:
> www.de.ufpe.br/~cysneiros  <http://www.de.ufpe.br/~cysneiros>
> http://150.161.44.6/cysneiros
>
> Lattes:
>
> *ResearchedID:*
> *Publons:*
> http://lattes.cnpq.br/1313497098151734
> http://www.researcherid.com/rid/G-6333-2012
> https://publons.com/a/1540091
> ORCID: http://orcid.org/0000-0001-6757-6969
>
>
> Description: Enter a Description
> <https://www.researcherid.com/ProfileGet.action?focus=description>
>
>
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