Robust beta regression through the logit transformation

Yuri S. Maluf, Silvia L. P. Ferrari () and Francisco F. Queiroz
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Yuri S. Maluf: University of São Paulo
Silvia L. P. Ferrari: University of São Paulo
Francisco F. Queiroz: University of São Paulo

Metrika: International Journal for Theoretical and Applied Statistics, 2025, vol. 88, issue 1, No 4, 81 pages

Abstract: 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, 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. The new R package robustbetareg is introduced.

Keywords: Beta regression; L $$_q$$ q -likelihood; Outliers; Proportional data; Robust estimators; Robust inference (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00184-024-00949-1

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