Van Niekerk, JanetBekker, Andriette, 1958-Arashi, Mohammad2019-02-052019-02-052019-12Van Niekerk, J., Bekker, A. & Arashi, M. 2019, 'Beta regression in the presence of outliers – a wieldy Bayesian solution', Statistical Methods in Medical Research, vol. 28, no. 12, pp. 3729-3740.0962-2802 (print)1477-0334 (online)10.1177/0962280218814574http://hdl.handle.net/2263/68411Real phenomena often leads to challenges in data. One of these is outliers or influential values. Especially in a small sample, these values can have a major influence on the modeling process. In the beta regression framework, this issue has been addressed mainly in two ways: the assumption of a different response model and the application of a minimum density power divergence estimation (MDPDE) procedure. In this paper, however, we propose a simple hierarchical Bayesian methodology in the context of a varying dispersion beta response model that is robust to outliers, as shown through an extensive simulation study and analysis of two real data sets. To robustify Bayesian modeling, a heavy-tailed Student's t prior with uniform degrees of freedom is adopted for the regression coefficients. This proposal results in a wieldy implementation procedure which avails practical use of the approach.en© The Author(s) 2018Beta regressionHeterogeneityOutlierRobust BayesStudent's tPostprint Article