Résumé
The link function is the key component of regression models for binary response variables. Despite the diverse potential fits obtained from different link functions, only the logit and the probit links have been widely popularized. Maximum likelihood estimations in models generated from these links are known to be non-robust in the presence of outliers. We show that this problem is exacerbated when the two response levels are strongly separated in the explanatory space. To address this shortcoming, we propose and encourage the use of the maximum likelihood estimation with the Student link function. We highlight its robustness to outliers and also to noisy variables, particularly when the data exhibit a strong separation setting, still keeping all the maximum likelihood estimation's properties.