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PLoS One. 2013 Apr 23;8(4):e61623. doi: 10.1371/journal.pone.0061623. Print 2013.

Boosted beta regression.

Author information

1
Department of Medical Informatics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany. matthias.schmid@imbe.med.uni-erlangen.de

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.

PMID:
23626706
PMCID:
PMC3633987
DOI:
10.1371/journal.pone.0061623
[Indexed for MEDLINE]
Free PMC Article
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