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

[Paulo C Rodrigues]

Prezados Colegas,

Temos o prazer de anunciar dois seminários, organizados na série
de Seminários em Estatística e Ciência de Dados, do Departamento de
Estatística da UFBA, em colaboração com o SaLLy (Statistical Learning
Laboratory, www.SaLLy.ufba.br). Informação abaixo.

Pedimos que partilhem com os colegas que possam participar.
Um abraço,
Paulo.

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*Palestrante*: Wagner Bonat (Universidade Federal do Paraná, PR, Brasil)
*Dia e hora*: 30/05/2023 às 15h00
*Local*: Auditório do Instituto de Matemática e Estatística da Universidade
Federal da Bahia (presencial)

*Title*: Multivariate Covariance Generalized Linear Models with
applications in R.

*Abstract*: In this talk I will present a recent proposed framework for
non-normal multivariate data analysis called multivariate covariance
generalized linear models (McGLMs), designed to handle multivariate
response variables, along with a wide range of temporal and spatial
correlation structures defined in terms of a generalized Kronecker product.
The models take non-normality into account in the conventional way by means
of a variance function, and the mean structure is modelled by means of a
link function and a linear predictor. The covariance structure is modelled
by means of a covariance link function combined with a matrix linear
predictor involving known matrices. The models are fitted using an
efficient Newton scoring algorithm based on quasi-likelihood and Pearson
estimating functions, using only second-moment assumptions. McGLMs provide
a unified approach to a wide variety of different types of response
variables and covariance structures, including multivariate extensions of
repeated measures, time series, longitudinal, spatial and spatio-temporal
data. Furthermore, I present the computational implementation in R through
the package mcglm. Illustrations include mixed models, longitudinal data
analysis, spatial models for areal data, models to deal with mixed outcomes
and multivariate models for count data using the Poisson-Tweedie
distribution.

*Bio*: Wagner Hugo Bonat is Researcher and Lecturer of the Department of
Statistics at Paraná Federal University - UFPR, where he has been since
2010. He is the Head of the Data Science and Big Data program (DSBD) and a
member of the Laboratory of Statistics and Geoinformation (LEG). He
received a B.S. from Paraná Federal University in 2008, and an M.S. from
the Paraná Federal University in 2010. He received his Ph.D. in Mathematics
and Computer Science from the University of Southern Denmark in 2016. His
research lies on statistical modelling and estimating functions. Much of
his work has been on extending the generalized linear model class to deal
with multiple response variables. His main contribution is a new class of
multivariate regression models called Multivariate Covariance Generalized
Linear models (McGLMs) and the associated R package (mcglm).
=========================================================

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*Palestrante*: Hugo Carvalho (Universidade Federal do Rio de Janeiro, RJ,
Brasil)
*Dia e hora*: 31/05/2023 às 16h00
*Local*: Auditório do Instituto de Matemática e Estatística da Universidade
Federal da Bahia (presencial)

*Title*: Bayesian restoration of audio degraded by low-frequency pulses
modeled via Gaussian process

*Abstract*: A common defect found when reproducing old vinyl and gramophone
recordings with mechanical devices are the long pulses with significant
low-frequency content caused by the interaction of the arm-needle system
with deep scratches or even breakages on the media surface. I will present
a novel Bayesian approach capable of jointly estimating the pulse location,
interpolating the almost annihilated signal underlying the strong
discontinuity that initiates the pulse, and also estimating the long pulse
tail by a simple Gaussian Process, allowing its suppression from the
corrupted signal. Controlled experiments indicate that the proposed method,
while requiring significantly less user intervention, achieves perceptual
results similar to those of previous approaches and performs well when
dealing with naturally degraded signals.

*Bio*: Hugo was born in São Paulo, Brazil, and received the Applied
Mathematics degree from the Federal University of Rio de Janeiro (UFRJ) in
2011, the M.Sc. degree from IM/UFRJ in Applied Mathematics in 2013
(monograph in Portuguese - An Introduction to Singularities in General
Relativity), and finally, in 2017, the D.Sc. degree from COPPE/UFRJ in
Electrical Engineering, area Signal Processing. Hugo is Assistant Professor
of the Department of Statistical Methods from the Federal University of Rio
de Janeiro, member of the MusMat Research Group and member of the editorial
board of MusMat • Brazilian Journal of Music and Mathematics. Fortunately,
he is able to merge his passions in his work: Music and Mathematics, being
applications of Statistics to this fascinating intersection, his main
research interest. Hugo is also interested in Music Information Retrieval,
Statistical Signal Processing and Computational Statistics. In his spare
time he likes to brew beer at home with his wife, play classical guitar,
learn how to play the harmonica, study music composition (and compose!) and
paint in watercolor.
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Paulo Canas Rodrigues
*Professor and Head*, Department of Statistics, Federal University of Bahia
<http://ufba.br>, Brazil
*Head and PI*, Statistical Learning Laboratory (SaLLy)
<http://sally.ufba.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/>
*Past-President*, Brazilian Region of the International Biometric Society
<http://www.rbras.org.br/>
*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/>, Braz. J. Biom.
<http://www.biometria.ufla.br/index.php/BBJ>

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