Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned
By Bettina Grün, Ioannis Kosmidis, Achim Zeileis. 2012
408 citations recorded by the source corpus.
Beta regression – an increasingly popular approach for modeling rates and proportions – is extended in various directions: (a) bias correction/reduction of the maximum likelihood estimator, (b) beta regression tree models by means of recursive partitioning, (c) latent class beta regression by means of finite mixture models. All three extensions may be of importance for enhancing the beta regression toolbox in practice to provide more reliable inference and capture both observed and unobserved/latent heterogeneity in the data. Using the analogy of Smithson and Verkuilen (2006), these extensions make beta regression not only “a better lemon squeezer” (compared to classical least squares regression) but a full-fledged modern juicer offering lemon-based drinks: shaken and stirred (bias correction and reduction), mixed (finite mixture model), or partitioned (tree model). All three extensions are provided in the R package betareg (at least 2.4-0), building on generic algorithms and implementations for bias correction/reduction, model-based recursive partioning, and finite mixture models, respectively. Specifically, the new functions betatree() and betamix() reuse the object-oriented flexible implementation from the R packages party and flexmix, respectively.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a119d505204ae10059b98d75607f9e41f54d9cd2
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Citations in the corpus
- Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020, neutral): “Model classes which provide the necessary building blocks include ‘lm’, ‘glm’, ‘mlm’, and ‘nls’ from stats (R Core Team 2018), ‘betareg’ from betareg (Cribari-Neto and Zeileis 2010; Grün et al. 2012), ‘clm’ from ordinal (Christensen 2018), ‘coxph’ and ‘survreg’ from survival (Therneau 2018), ‘crch’ ”
- Analysing continuous proportions in ecology and evolution: A practical introduction to beta and Dirichlet regression (2019, methodology): “…estimators can lead to erroneous inference, and methods for bias–reduction and bias–correction are an active research area in statistics and in beta regression in particular (Grün et al., 2012; Kosmidis, 2014; Kosmidis & Firth, 2010; Ospina, Cribari‐Neto, & Vasconcellos, 2006; Simas et al., 2010).”