Partial Separation in Logistic Discrimination
研究了逻辑判别中最大似然估计不存在的部分分离问题，证明了非存在定理并开发了区分分离与多重共线性的算法，对处理分类数据中的聚类分离有实际指导意义。
SUMMARY The problem of maximum likelihood estimates in logistic discrimination is receiving growing attention in the literature. The existence is known to be highly dependent on the data configuration observed. Here we focus on the practically important case where the group samples form a set of clusters that are completely separated from each other, which we call ‘partial separation'. The classical Fisher iris data set is a good example of the problem discussed. A non-existence theorem is proven and a general algorithm is developed to help to distinguish separation from multicollinearity given divergence in maximum likelihood estimation.