时 间：2020年10月26日（周一） 09：30-10：30
地 点：Zoom会议 ID： 689 9105 6390
题 目：MODEL CHECKING IN MASSIVE DATASET VIA STRUCTURE-ADAPTIVE-SAMPLING
主讲人：王兆军 南开大学统计与数据科学学院执行院长、教授
摘 要：Lack-of-fit testing is often essential in many applications of statistical/machine learning. Despite the availability of large datasets, in many applications, collecting labels for all data points is impossible due to measurement constraints. We propose a design-adaptive testing procedure to check a model when only a limited number of responses can be accessed. To select a small subset of covariates from a large pool of given design points, we derive an optimal sampling strategy, the structure-adaptive-sampling, with which the proposed test possesses the asymptotically best power. Numerical results on both synthetic and real-world data confirm the effectiveness of the proposed method.
报告人简介：
王兆军，南开大学统计与数据科学学院执行院长、教授，国务院学位委员会统计学科评议组成员、国家统计专家咨询委员会委员、中国现场统计研究会副理事长、中国工业统计教学研究会副会长、天津工业与应用数学学会理事长，曾获国务院政府特贴、全国百篇优博指导教师及天津市自然科学一等奖。
