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Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned

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  • Bettina Grün
  • Ioannis Kosmidis
  • Achim Zeileis

Abstract

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.

Suggested Citation

  • Bettina Grün & Ioannis Kosmidis & Achim Zeileis, 2011. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Working Papers 2011-22, Faculty of Economics and Statistics, Universität Innsbruck.
    • Handle: RePEc:inn:wpaper:2011-22
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    References listed on IDEAS

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    Cited by:

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    5. Zhou, Haiming & Huang, Xianzheng, 2022. "Bayesian beta regression for bounded responses with unknown supports," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    6. Cristine Rauber & Francisco Cribari-Neto & Fábio M. Bayer, 2020. "Improved testing inferences for beta regressions with parametric mean link function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 687-717, December.
    7. Irina-Eugenia Iamandi & Laura-Gabriela Constantin & Sebastian Madalin Munteanu & Bogdan Cernat-Gruici, 2024. "Insights on the Relationship between Artificial Intelligence Skills and National Culture," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 741-741, August.
    8. Wladislaw Mill & John Morgan, 2022. "The cost of a divided America: an experimental study into destructive behavior," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 974-1001, June.
    9. Chen, Kee Kuo & Chiu, Rong-Her & Chang, Ching-Ter, 2017. "Using beta regression to explore the relationship between service attributes and likelihood of customer retention for the container shipping industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 1-16.
    10. Tariq Maqsood & Mark Edwards & Ioanna Ioannou & Ioannis Kosmidis & Tiziana Rossetto & Neil Corby, 2016. "Seismic vulnerability functions for Australian buildings by using GEM empirical vulnerability assessment guidelines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1625-1650, February.
    11. Diederik Strubbe & Laura Jiménez & A. Márcia Barbosa & Amy J. S. Davis & Luc Lens & Carsten Rahbek, 2023. "Mechanistic models project bird invasions with accuracy," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
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    More about this item

    Keywords

    beta regression; bias correction; bias reduction; recursive partitioning; finite mixture; R;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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