中文摘要：

二次的判别式分析是一个古典、流行的分类工具，但是它没能处于尺寸 p 比样品尺寸 n 大的高度维的状况工作。处理这个问题，作者经由以一种连续方式屏蔽相关预言者减少错误分类建议一个山脉前面的二次的判别式(RFQD ) 分析方法率。作者使用扩大贝叶斯的信息标准决定最后的模型并且证明 RFQD 是选择一致。蒙特卡罗模拟被进行检验它的表演。

英文摘要：

Quadratic discriminant analysis is a classical and popular classification tool, but it fails to work in high-dimensional situations where the dimension p is larger than the sample size n. To address this issue, the authors propose a ridge-forward quadratic discriminant （RFQD） analysis method via screening relevant predictors in a successive manner to reduce misclassification rate. The authors use extended Bayesian information criterion to determine the final model and prove that RFQD is selection consistent. Monte Carlo simulations are conducted to examine its performance.