Performance of asymmetric links and correction methods for imbalanced data in binary regression

ABSTRACT

In binary regression, imbalanced data result from the presence of values equal to zero (or one) in a proportion that is significantly greater than the corresponding real values of one (or zero). In this work, we evaluate two methods developed to deal with imbalanced data and compare them to the use of asymmetric links. The results based on simulation study show, that correction methods do not adequately correct bias in the estimation of regression coefficients and that the models with power links and reverse power considered produce better results for certain types of imbalanced data. Additionally, we present an application for imbalanced data, identifying the best model among the various ones proposed. The parameters are estimated using a Bayesian approach, considering the Hamiltonian Monte-Carlo method, utilizing the No-U-Turn Sampler algorithm and the comparisons of models were developed using different criteria for model comparison, predictive evaluation and quantile residuals.

Keywords:

• Asymmetric link
• binary regression
• imbalanced data
• predictive evaluation
• quantile residuals
• similarity measures

Acknowledgments

The authors also thank the Editor, the Associate Editor, and the Referees for their useful comments and suggestions, which resulted in an improvement in the original version of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

J. L. Bazán  http://orcid.org/0000-0003-3918-8795 D. K. Dey  http://orcid.org/0000-0002-9367-9731

Additional information

Funding

The first author thanks the support from CAPES-Brazil. The second author was partially supported by Fundação de Amparo à Pesquisa do Estado de São Paulo FAPESP-Brazil 2017/15452-5. The third author was supported by FAPESP and CAPES-Brazil.