Variable Selection in Heteroscedastic Discriminant Analysis
该文利用似然比检验统计量的逐步分解，评估每个变量对均值与协方差矩阵差异的贡献，并据此提出三种变量选择方法，适用于异方差判别分析。
Abstract The likelihood ratio test statistic for the identity in means and covariance matrices of k normal populations has a well-known step-down decomposition measuring the contribution of each component of the vector observation. This decomposition in turn gives rise to three components testing the residual homo-scedasticity of each variable, the parallelism of its regression on its predecessors, and the identity of location. A variety of uses of this decomposition in selecting variables is proposed.