Boosted Beta Regression

Matthias Schmid, Florian Wickler, Kelly O Maloney, Richard Mitchell, Nora Fenske and Andreas Mayr

PLOS ONE, 2013, vol. 8, issue 4, 1-15

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.

Date: 2013
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0061623

DOI: 10.1371/journal.pone.0061623

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