A Polynomial Algorithm for Best Subset Selection
时间：2022-04-13
阅读量：563次
- 演讲人： 王学钦（中国科学技术大学 教授）
- 时间：2022年04月22日 周五 15:00（北京时间）
- 地点：腾讯会议 935-419-590
- 主办单位：数据科学研究中心
摘要：Best subset selection aims to find a small subset of predictors that lead to the most desirable and pre-defined prediction accuracy in a linear regression model. It is not only the most fundamental problem in regression analysis, but also has far reaching applications in every facet of research including computer science and medicine. We introduce a polynomial algorithm which under mild conditions, solves the problem. This algorithm exploits the idea of sequencing and splicing to reach the stable solution in finite steps when the sparsity level of the model is fixed but unknown. We define a novel information criterion that the algorithm uses to select the true sparsity level with a high probability. We show when the algorithm produces a stable optimal solution that is the oracle estimator of the true parameters with probability one. We also demonstrate the power of the algorithm in several numerical studies.
个人简介：王学钦，中国科学技术大学管理学院教授。2003年毕业于纽约州立大学宾厄姆顿分校。他现担任教育部高等学校统计学类专业教学指导委员会委员、中国现场统计研究会副理事长，统计学国际期刊《JASA》等的Associate Editor、高等教育出版社《Lecture Notes: Data Science, Statistics and Probability》系列丛书的副主编。
联系人：蒋杭进（jianghj@zju.edu.cn）
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