Robust Logistic Discrimination
本文改进了逻辑判别模型，使其对异常观测值更稳健，并通过模拟数据和老鼠牙齿数据验证了其优于普通逻辑判别和抗性参数拟合方法。
Logistic discrimination is a well established method for allocating observations to one of two or more populations. In this paper we show how the logistic model can be adapted to make it robust against outlying observations from the populations. The new model is successfully tested using simulated data against ordinary logistic discrimination and also against logistic discrimination where parameters are fitted using resistant methods. The model is then used on data collected from the teeth of rats.