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学术报告

A Subsampling Method for Regression Problems Based on Minimum Energy Criterion

王典朋 副研究员

（北京理工大学）

报告时间： 2023年12月11日 (星期一) 下午4：00-5：00

报告地点：沙河主E706

报告摘要：The extraordinary amounts of data generated nowadays pose heavy demands on computational resources and time, which hinders the implementation of various statistical methods. An efficient and popular strategy of downsizing data volumes and thus alleviating these challenges is subsampling. However, the existing methods either rely on specific assumptions for the underlying models or acquire partial information from the available data. For regression problems, we propose a novel approach, termed adaptive subsampling with the minimum energy criterion (ASMEC). The proposed method requires no explicit model assumptions and “smartly” incorporates information on covariates and responses. ASMEC subsamples possess two desirable properties: space-fillingness and spatial adaptiveness. We investigate the limiting distribution of ASMEC subsamples and their theoretical properties under the smoothing spline regression model. The effectiveness and robustness of the ASMEC approach are also supported by a variety of synthetic examples and two real-life examples.

报告人简介：王典朋，北京理工大学特别副研究员，博士生导师。王典朋副研究员在北京理工大学获得博士学位，中国科学院数学与系统科学研究院博士后，曾先后访问佐治亚理工、香港科技大学，担任北京大数据协会常务理事、中国现场统计研究会试验设计分会理事。主要从事敏感性试验设计、计算机试验设计、贝叶斯计算等方向的研究。主持国家自然科学基金青年基金、面上项目和国家先进星箭共性技术等项目多项，在Technometrics、Journal of Quality Technology、Statistica Sinica、RESS、Applied Mathematical Modelling等统计学权威期刊上发表论文多篇。

邀请人：罗雪