Linear Discriminant Analysis with Misallocation in Training Samples
研究了两类线性判别分析在训练样本存在错误分类时的表现，推导了混合分布的均值向量和协方差矩阵，给出了判别边界的渐近分布以及错误率的前两阶矩，并通过数值结果展示了随机和两种非随机错误分类模型的影响。
Abstract Linear discriminant analysis for a two-class case is studied in the presence of misallocation in training samples. A general approach to modeling of misallocation is formulated, and the mean vectors and covariance matrices of the mixture distributions are derived. The asymptotic distribution of the discriminant boundary is obtained, and the asymptotic first two moments of the error rates are given. Certain numerical results for the error rates are presented by considering the random and two nonrandom misallocation models.