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 autho...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.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.11401391
文摘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.
