On beta regression residuals

By Patrícia Leone Espinheira, Silvia L. P. Ferrari, Francisco Cribari‐Neto. 2008

199 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ec9659c7d1a6dc387bacc920d0d4390278b69545

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Improved likelihood inference in beta regression (2011, background): “[3,4] for a full account of large-sample inference and diagnostics in the beta regression model with constant precision.”
Analysing continuous proportions in ecology and evolution: A practical introduction to beta and Dirichlet regression (2019, methodology): “See Espinheira et al. (2008) for a range of expressions for the calculation of standardized residuals, and a detailed discussion of their relative merits.”
Mixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution (2012, methodology): “Epsinheira, Ferrari, and Cribari-Neto (2008a, 2008b) explore alternative weighted standardized residuals and influence diagnostics for the Ferrari-Cribari-Neto beta GLM. Buckley (2002) implemented the Paolino model in a Bayesian Markov chain Monte Carlo (MCMC) procedure, and Smithson and Verkuilen…”
The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation (2012, background): “…(van Dorp and Mazzuchi 2004), and to develop and estimate regression models (Cribari-Neto and 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…”
A quantile parametric mixed regression model for bounded response variables (2017, methodology): “The beta regression model is sufficiently documented in several publications such as [8], [9], [12], and [6] and in several applications like in [22] and [37].”
A New Robust Regression Model for Proportions (2012, methodology): “Also, a general Metropolis-Hastings algorithm could run much faster in a programming language, for example using C++ with R (Eubank and Kupresanin 2012).”
Diagnostic tools in beta regression with varying dispersion (2011, methodology): “For instance, in Espinheira et al. (2008b) these plots were able to clearly indicate that the model dispersion was variable and also suggested which variables should be used in the dispersion modelling.”
A general class of zero-or-one inflated beta regression models (2011, methodology): “Beta regression models have been studied by Kieschnick andMcCullough (2003), Ferrari and Cribari-Neto (2004), Espinheira et al. (2008a,b), Paolino (2001), Smithson and Verkuilen (2006), Korhonen et al. (2007), Simas et al. (2010) and Ferrari and Pinheiro (2010), among others. Oftentimes, proportions”
Beta Regression in R (2010, background): “…proposed by Espinheira, Ferrari, and Cribari-Neto (2008b), in particular one residual with better properties that they named standardized weighted residual 2 : where ) } and h ii , the i th diagonal element of the hat matrix (for details see Ferrari and Cribari-Neto 2004; Espinheira et al. 2008b).”