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Bayesian outlier analysis in binary regression

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  • Aparecida Souza
  • Helio Migon

Abstract

We propose alternative approaches to analyze residuals in binary regression models based on random effect components. Our preferred model does not depend upon any tuning parameter, being completely automatic. Although the focus is mainly on accommodation of outliers, the proposed methodology is also able to detect them. Our approach consists of evaluating the posterior distribution of random effects included in the linear predictor. The evaluation of the posterior distributions of interest involves cumbersome integration, which is easily dealt with through stochastic simulation methods. We also discuss different specifications of prior distributions for the random effects. The potential of these strategies is compared in a real data set. The main finding is that the inclusion of extra variability accommodates the outliers, improving the adjustment of the model substantially, besides correctly indicating the possible outliers.

Suggested Citation

  • Aparecida Souza & Helio Migon, 2010. "Bayesian outlier analysis in binary regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1355-1368.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1355-1368
    DOI: 10.1080/02664760903031153
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    References listed on IDEAS

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    1. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
    2. Thaís C. O. Fonseca & Marco A. R. Ferreira & Helio S. Migon, 2008. "Objective Bayesian analysis for the Student-t regression model," Biometrika, Biometrika Trust, vol. 95(2), pages 325-333.
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    Cited by:

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