Robit Regression: A Simple Robust Alternative to Logistic and Probit Regression

@inproceedings{Liu2005RobitRA,
  title={Robit Regression: A Simple Robust Alternative to Logistic and Probit Regression},
  author={Chuanhai Liu},
  year={2005},
  url={https://api.semanticscholar.org/CorpusID:3110460}
}
Logistic and probit regression models are commonly used in practice to analyze binary response data, but the maximum likelihood estimators of these models are not robust to outliers. This paper considers a robit regression model, which replaces the normal distribution in the probit regression model with a t-distribution with a known or unknown number of degrees of freedom. It is shown that (i) the maximum likelihood estimators of the robit model with a known number of degrees of freedom are… 
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