[ABE-L] Ciclo de Seminarios PPGEst-UFRGS (Silvia Ferrari - IME, USP - 20/06 às 13:30)

[RENATA ROJAS GUERRA]

Prezados,

Temos o prazer de convidar a todos para o segundo seminário do Programa de
Pós-Graduação em Estatística da UFRGS (PPGEst-UFRGS)!
Informações abaixo:

*Palestrante:* Silvia L. P. Ferrari (IME, USP)
*Data:* 20 de junho de 2023 (terça-feira) às 13h30min.
*Link:* mconf.ufrgs.br/webconf/ppgest

*Título:* Building non-separable spatio-temporal models

*Resumo:* Beta regression models are employed to model continuous response
variables in the unit interval, like rates, percentages, or proportions.
Their applications rise in sev- eral areas, such as medicine, environment
research, finance, and natural sciences. The maximum likelihood estimation
is widely used to make inferences for the parameters. Nonetheless, it is
well-known that the maximum likelihood-based inference suffers from the
lack of robustness in the presence of outliers. Such a case can bring
severe bias and misleading conclusions. Recently, robust estimators for
beta regression models were presented in the literature. However, these
estimators require non-trivial restrictions in the parameter space, which
limit their application. This paper develops new robust estimators that
overcome this drawback. Their asymptotic and robustness properties are
studied, and robust Wald-type tests are introduced. Simulation results
evidence the merits of the new robust estimators. Inference and diagnostics
using the new estimators are illustrated in an application to health
insurance coverage data.



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