Improved maximum likelihood estimation in a new class of beta regression models

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Mixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution (2012, methodology): “Likelihood maximization has been the dominant approach in estimating beta regression models (e.g., Paolino, 2001; Smithson & Verkuilen, 2006), with Vasconcellos and Cribari-Neto (2005) and Ospina, Cribari-Neto, and Vasconcellos (2006) proposing bias-correcting adjustments.”
Improved estimators for a general class of beta regression models (2008, methodology): “To work with this distribution in a regression manner, several models were defined, see for instance, Ferrari and Cribari-Neto (2004), Kieschnick and McCullough (2003), Paolino (2001), Vasconcellos and Cribari-Neto (2005), among others.”
The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation (2012, background): “…Souza Forthcoming; Cribari-Neto and Zeileis 2010; Espinheira et al 2008a,b; Ferrari and Cribari-Neto 2004; Ferrari et al. 2011; Kieschnick and McCullough 2003; Ospina et al 2006; Paolino 2001; Rocha and Simas 2011; Simas et al 2010; Smithson and Verkuilen 2006; Vasconcellos and Cribari-Neto 2005).”