A new robust regression model for proportions

Cristian L. Bayes, Jorge L. Bazán, Catalina García

    Research output: Contribution to journalArticlepeer-review

    85 Scopus citations

    Abstract

    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 influence of outliers by considering contaminated data under four perturbation patterns to generate outliers were carried out and confirm 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 coeficients, 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.

    Original languageEnglish
    Pages (from-to)841-866
    Number of pages26
    JournalBayesian Analysis
    Volume7
    Issue number4
    DOIs
    StatePublished - 2012

    Keywords

    • Bayesian estimation
    • Beta regression
    • Link function
    • MCMC
    • Proportions

    Fingerprint

    Dive into the research topics of 'A new robust regression model for proportions'. Together they form a unique fingerprint.

    Cite this