A0190
Title: Robust beta regression through the logit transformation
Authors: Francisco F Queiroz - University of Sao Paulo (Brazil) [presenting]
Silvia Ferrari - University of Sao Paulo (Brazil)
Yuri Maluf - University of Sao Paulo (Brazil)
Abstract: Beta regression models are employed to model continuous response variables in the unit interval, like rates, percentages, or proportions. Their applications rise in several areas, such as medicine, environmental research, finance, and natural sciences. The maximum likelihood estimation is widely used to make inferences about 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. New robust estimators that overcome this drawback are developed. 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.
