A New Robust Regression Model for Proportions

By C. Bayes, Jorge L. Bazán, Catalina Beatriz García García. 2012

116 citations recorded by the source corpus.

A new regression model for proportions is presented by considering the Beta rectangular distribution proposed by Hahn (2008). This new model includes the Beta regression model introduced by Ferrari and Cribari-Neto (2004) and the variable dispersion Beta regression model introduced by Smithson and Verkuilen (2006) as particular cases. Like Branscum, Johnson, and Thurmond (2007), a Bayesian inference approach is adopted using Markov Chain Monte Carlo (MCMC) algorithms. Simulation studies on the in∞uence of outliers by considering contam- inated data under four perturbation patterns to generate outliers were carried out and conflrm that the Beta rectangular regression model seems to be a new robust alternative for modeling proportion data and that the Beta regression model shows sensitivity to the estimation of regression coe-cients, to the posterior distribution of all parameters and to the model comparison criteria considered. Furthermore, two applications are presented to illustrate the robustness of the Beta rectangular model.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2e068a9817fd5feddbe0c14baf26e2239be0fa6e

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In Statistics and Probability

1. Mário de Castro
2. C. Bayes
3. Jorge L. Bazán
4. Gauss Moutinho Cordeiro
5. Edwin M. M. Ortega

Citations in the corpus

• A quantile parametric mixed regression model for bounded response variables (2017, methodology): “In addition, the beta rectangular model proposed by [1] is more robust to outliers (comparatively large or influential values of the response variable) than the beta regression model.”