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

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Author Info

  • 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.

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File URL: http://eeecon.uibk.ac.at/wopec2/repec/inn/wpaper/2011-22.pdf
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Bibliographic Info

Paper provided by Faculty of Economics and Statistics, University of Innsbruck in its series Working Papers with number 2011-22.

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Length: 31
Date of creation: Oct 2011
Date of revision:
Handle: RePEc:inn:wpaper:2011-22

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Keywords: beta regression; bias correction; bias reduction; recursive partitioning; finite mixture; R;

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