Speaker

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　　Abstract

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.

　　Affiliation

王兆军，南开大学统计与数据科学学院执行院长/教授 教育部长江学者特聘教授 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