Development of Statistical Discriminant Mathematical Programming Model Via Resampling Estimation Techniques
利用刀切法和自助法等重抽样技术，开发了一个用于判别分析问题的统计数学规划模型，能识别显著参数并提升分类性能，对研究判别模型的学者有参考价值。
Abstract This paper uses resampling estimation techniques to develop a statistical mathematical programming model for discriminant analysis problems. Deleted‐d jackknife, deleted‐d bootstrap, and bootstrap procedures are used to identify statistical significant parameter estimates for a discriminant mathematical programming (MP) model. The results of this paper indicate that the resampling approach is a viable model selection technique. Furthermore, estimating the MP models via resampling techniques can also improve the classification performance compared to a deterministic discriminant MP model. In this study, the deleted‐d jackknife procedure was the most promising among the resampling estimation techniques examined.