[ABE-L] (sem assunto)

[Vinicius Mayrink]

Caros,

Na próxima sexta-feira (*13 de maio*, às *13:30h*) o ciclo de *Seminários
do Departamento de Estatística da UFMG* terá a apresentação de *Silvia L.
P. Ferrari*.

Silvia é professora titular do Departamento de Estatística do IME/USP em
São Paulo. Ela obteve o grau de Doutora em Estatística pela USP. Suas
principais áreas de pesquisa são: Regressão, Regressão beta e refinamento
de métodos assintóticos .

O seminário será transmitido ao vivo pelo canal do Youtube "Seminários DEST
- UFMG <https://www.youtube.com/channel/UCoZC2_pME9ca_-Hx4djd60w>".

At.te,
Vinícius Mayrink


*********** Título e Resumo ***********
Silvia L. P. Ferrari (Departamento de Estatística, IME, USP)


*Robust estimation in beta regression via maximum Lq-likelihood.*
Beta regression models are widely used for modeling continuous data limited
to the unit interval, such as proportions, fractions, and rates. The
inference for the parameters of beta regression models is commonly based on
maximum likelihood estimation. However, it is known to be sensitive to
discrepant observations. In some cases, one atypical data point can lead to
severe bias and erroneous conclusions about the features of interest. In
this work, we develop a robust estimation procedure for beta regression
models based on the maximization of a reparameterized Lq-likelihood. The
new estimator offers a trade-off between robustness and efficiency through
a tuning constant. To select the optimal value of the tuning constant, we
propose a data-driven method that ensures full efficiency in the absence of
outliers. We also improve on an alternative robust estimator by applying
our data-driven method to select its optimum tuning constant. Monte Carlo
simulations suggest marked robustness of the two robust estimators with
little loss of efficiency when the proposed selection scheme for the tuning
constant is employed. Applications to three datasets are presented and
discussed. As a by-product of the proposed methodology, residual diagnostic
plots based on robust fits highlight outliers that would be masked under
maximum likelihood estimation. Joint work with Terezinha K. A. Ribeiro.

-- 
*Vinícius D. Mayrink*
*Professor Associado - Departamento de Estatística*

*ICEx, Universidade Federal de Minas Gerais*
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