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Boosted Beta Regression

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Listed:
  • Matthias Schmid
  • Florian Wickler
  • Kelly O Maloney
  • Richard Mitchell
  • Nora Fenske
  • Andreas Mayr

Abstract

: Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

Suggested Citation

  • Matthias Schmid & Florian Wickler & Kelly O Maloney & Richard Mitchell & Nora Fenske & Andreas Mayr, 2013. "Boosted Beta Regression," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-15, April.
  • Handle: RePEc:plo:pone00:0061623
    DOI: 10.1371/journal.pone.0061623
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    References listed on IDEAS

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