数学与统计学院学术报告[2020] 088号

(高水平大学建设系列报告441号)

报告题目:  A Polynomial Algorithm for Best Subset Selection Problem

报告人：王学钦教授（中国科学技术大学）

报告时间：2020年11月12日周四晚19:30-20:30　　　　　　　　　　　　 　　

报告地点：腾讯会议 会议号码：588910114　　　　　　　　　　　　　　　　　　　

报告内容：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 use an  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.

报告人简历:  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。2013年获得国家优秀青年研究基金。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、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。

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数学与统计学院

2020年11月9日