[ABE-L] Seminários em Estatística e Ciência de Dados - Departamento de Estatística da UFBA

[Paulo C Rodrigues]

Prezados Colegas,

Na próxima terça-feira, dia 2*8 de setembro, às 16h30*, vamos dar
continuidade aos Seminários em Estatística e Ciência de Dados da
Universidade Federal da Bahia.

Este seminário será ministrado pelo *Professor Eufrasio de Andrade Lima
Neto*.

Eufrásio is an Associate Professor in the Department of Statistics and
faculty member of the Graduate Program in Computational and Mathematical
Modelling at the Federal University of Paraíba. He has Bachelor’s and
Master’s degrees in Statistics and Ph.D. in Computer Science (Machine
Learning) from Federal University of Pernambuco, Brazil. His main research
interests are statistical modeling, regression, generalized linear models,
robust regression, clusterwise regression, machine learning, symbolic data
analysis, interval-valued data and kernel methods. He is the author of over
50 technical papers in international journals and conferences. He was a
Member of the Board of Directors of the Latin American Regional Section of
the International Association of Statistics Computing and Executive
Secretary of the Brazilian Association of Statistics. Currently, he is
Member of the Board of Directors of the Brazilian Association of Statistics
and an ISI Elected Member.


Os detalhes do seminários são os seguintes:

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*Title*: Interval joint robust regression method

*Abstract*: Interval-valued data are needed to manage either the
uncertainty related to measurements, or the variability inherent to the
description of complex objects representing a group of individuals. A
number of regression methods suitable to interval variables describing
variability of complex objects are already available. However, less
attention has been given to methods that, simultaneously, take into account
the full interval information and are resistant to interval outlier
observations, even with the frequent presence of atypical observations on
interval-valued data sets. This paper proposes a new robust linear
regression method for interval variables, where the presence of outliers
either in the center or in the radius penalizes both the center and the
radius regression models. Moreover, the interval observations with outliers
on both center and radius are more penalized than those observations with
outliers only in the center (or in the radius).  Besides, this paper
provides a suitable iterative algorithm to estimate the parameters of the
proposed method. The algorithm estimates the parameters of the center (or
of the radius) model taking into account both information of the center and
the radius. The convergence and time complexity of the iterative algorithm
are also presented. Finally, the performance of the new method is compared
with some previous robust regression approaches and evaluated on synthetic
and real interval-valued data sets.

*Data*: 16h30 do dia 28 de setembro de 2021

*Transmissão ao vivo online em*: meet.google.com/zuv-vvue-tuz.

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Partilhem com os vossos colegas!

São todos bem-vindos!

Um abraço,
Paulo Canas Rodrigues
Paulo Henrique Ferreira

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Paulo Canas Rodrigues
*Professor* at Federal University of Bahia <http://ufba.br>, Brazil
*President*, Brazilian Region of the International Biometric Society
<http://www.rbras.org.br>
*President-Elect, International Society for Business and
<https://www.isbis-isi.org/>**Industrial* *Statistics*
<https://www.isbis-isi.org/>
*(Founding) Chair,SIG on Data Science, of the International Statistical
Institute <https://www.isi-web.org/>*
*Vice-Coordinator*, Specialization in Data Science and Big Data
<http://ecd.ufba.br>
*Member of the Board of Directors, *Brazilian Statistical Association
<https://www.redeabe.org.br/>
*Editor for*: CompStat <https://www.springer.com/journal/180>, SOIC
<http://www.iapress.org/index.php/soic>, Biom. Letters
<http://www.up.poznan.pl/biometrical.letters/>, Rev.Bras.Biom.
<http://www.biometria.ufla.br/index.php/BBJ>

*CV Lattes*: http://lattes.cnpq.br/0029960374321970
*Web*: www.paulocanas.org
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