Beta Regression for Modelling Rates and Proportions

By Silvia L. P. Ferrari, Francisco Cribari‐Neto. 2004

3,028 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/09b8ab5c04d6a914ae9a7157f7de8880205165fd

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Local Models for Forest Canopy Cover with Beta Regression (2007, methodology): “estimation procedure differs from the generalized linear models since the beta distribution does not belong to the exponential family (Ferrari and Cribari-Neto 2004).”
Generalized beta regression models for random loss given default (2008, methodology): “We start the explanation of the GBR framework with the beta regression model proposed in Ferrari and Cribari-Neto (2004).”
Alternative Estimating and Testing Empirical Strategies for Fractional Regression Models (2011, positive): “As found independently by Paolino (2001) and Ferrari and Cribari-Neto (2004), the interpretation of the parameters of the beta regression model is greatly simplified if a mean-dispersion parameterization of the beta density is used.”
A general class of zero-or-one inflated beta regression models (2011, methodology): “Beta regression models have been studied by Kieschnick & McCullough (2003), Ferrari & Cribari-Neto (2004), Espinheira, Ferrari & Cribari-Neto (2008a, 2008b), Paolino (2001), Smithson & Verkuilen (2006), Korhonen et al. (2007), Simas, BarretoSouza & Rocha (2010), and Ferrari & Pinheiro (2010), among…”
On the one parameter unit-Lindley distribution and its associated regression model for proportion data (2018, methodology): “For the sake of comparison we also fit the Beta regression model [2,8].”
Mixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution (2012, methodology): “Apparently independently, Ferrari and Cribari-Neto (2004) derived a similar beta regression model that recently has been implemented in the SAS GLIMMIX procedure (SAS, 2008), and likewise Kieschnick and McCullogh (2003) compared the performance of a beta regression model for proportions, as…”
Kumaraswamy autoregressive moving average models for double bounded environmental data (2017, background): “This is also true in the Engineering literature, as, for instance, in Sundar and Subbiah (1989), Fletcher and Ponnambalam (1996), Seifi et al. (2000), Ponnambalam et al. (2001), Ganji et al. (2006), and Koutsoyiannis and Xanthopoulos (1989).”
Methods for estimating aboveground biomass and its components for Douglas-fir and lodgepole pine trees (2016, methodology): “This was expected because the measurement of proportion takes values on the open interval (0, 1), and the influence of explanatory variables on continuous responses bounded between 0 and 1 can be investigated with the beta regression (Ferrari and Cribari-Neto 2004).”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “The decision trees created by C4.5 can be utilized for classification Ferrari and Cribari-Neto (2004); Shafi and Abbass (2013); Laskov, et al. (2005) SMO classifier 97% detection rate.”
Improved estimators for a general class of beta regression models (2008, methodology): “Furthermore, the score vector (5) becomes Uβ(β, φ) = φX̃ T (y − μ), Uφ(β, φ) = ∑n i=1 vi, where T is as defined in equation (16), and vi are defined in page 4.”
Inflated beta distributions (2007, methodology): “In this work, we propose mixed continuous-discrete distributions to model data that are observed on [0,1), (0,1] or [0,1].”
A New Robust Regression Model for Proportions (2012, methodology): “The Beta regression model was introduced by Ferrari and Cribari-Neto (2004) and it is adequate for situations where the variable of interest is continuous, restricted to the interval (0, 1) — such as percentages, proportions and fractions or rates (Kieschnick and McCullough, 2003) — and related to…”
Some New Facts about the Unit-Rayleigh Distribution with Applications (2020, background): “The unit-distributions are of particular interest because of the following argues: (i) any rv X with bounded support of the form (0, a), with a > 0, can be rescaled on (0, 1) as Y = X/a, and thus Y follows a certain unit-distribution, (ii) the unit-distributions allow us to define general and simple”
On beta regression residuals (2008, methodology): “In the beta regression model [1] the mean of yt is written as g(μt) = ∑ki=1 xtiβi = ηt , where β = (β1, .”
Characterizing forest structural types and shelterwood dynamics from Lorenz-based indicators predicted by airborne laser scanning (2013, methodology): “which were, therefore, estimated by maximum likelihood for fitting of the beta distribution to the plot-level sample (Ferrari and Cribari-Neto 2004).”