  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, 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.
