[ABE-L] Seminário DEST/UFMG em 02/06/2023

[Marcos Prates]

Caros,

Na próxima sexta-feira (02 de Junho, às 13:30h) o ciclo de Seminários do
Departamento de Estatística da UFMG terá a apresentação do profa. Clarice
Demétrio da ESALQ.

Clarice G. B. Demétrio is a Professor of Experimental Statistics at the
Department of Exact Sciences at Escola Superior de Agricultura “Luiz de
Queiroz”, University of São Paulo, Brazil. She has a bachelor in Agronomy,
Masters and PhD in Applied Statistics in Agriculture from the University of
São Paulo, and a Post-Doctoral training at the Matemathics Department,
Imperial College of Science and Technology, London, England. She also has a
Doctor Honoris Causa from Hasselt University, Belgium, 2019. She got the
“Herman Callaert Leadership Award in Biostatistical Education”, Center for
Statistics, Hasselt University, Diepenbeek, Belgium in 2006; the award
“Best Contributed Paper from a Special Circumstance for the Americas”,
during the IBC2008, in 2008, Dublin, Ireland; the “Premio Anual del
Proyecto Juárez Lincoln Marti”, in 2009, the “Rob Kempton Award for
Outstanding Contribution to the Development of Biometry in the Developing
World, IBC2010 and the Pesquisador 2022 Award, ABE. Clarice has served as
President of the International Biometric Society during the 2012-2013 term.
Título: Extended Poisson-Tweedie: properties and regression models for
count data

Resumo: We propose a new class of discrete generalized linear models based
on the class of Poisson-Tweedie factorial dispersion models with variance
of the form , where $ is the mean, and p are the dispersion and Tweedie
power parameters, respectively (Bonat et al, 2018; 18: 24–49). The models
are fitted by using an estimating function approach obtained by combining
the quasi-score and Pearson estimating functions for estimation of the
regression and dispersion parameters, respectively. This provides a
flexible and efficient regression methodology for a comprehensive family of
count models including Hermite, Neyman Type A, Pólya-Aeppli, negative
binomial and Poisson-inverse Gaussian. The estimating function approach
allows us to extend the Poisson-Tweedie distributions to deal with
underdispersed count data by allowing negative values for the dispersion
parameter . Furthermore, the Poisson-Tweedie family can automatically adapt
to highly skewed count data with excessive zeros, without the need to
introduce zero-inflated or hurdle components, by the simple estimation of
the power parameter. Thus, the proposed models offer a unified framework to
deal with under, equi, overdispersed, zero-inflated and heavy-tailed count
data. The computational implementation of the proposed models is fast,
relying only on a simple Newton scoring algorithm. Simulation studies
showed that the estimating function approach provides unbiased and
consistent estimators for both regression and dispersion parameters. We
highlight the ability of the Poisson-Tweedie distributions to deal with
count data through a consideration of dispersion, zero-inflated and heavy
tail indexes, and illustrate its application with four data  analyses.

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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