报告题目：Optimal subsampling for multiplicative linear measurement error models

报告时间：2026年9月13日上午9:30

报告地点：北湖东校区数统新楼201室

主办单位：激情视频

报告人：王明秋

报告人简介：王明秋，曲阜师范大学统计与数据科学学院教授，博士生导师。研究方向包括稳健估计、非参数统计推断、高维数据分析、大数据抽样等。先后主持国家自然科学基金面上项目等省部级以上项目7项。主要成果发表在国内外知名学术刊物SCIENCE CHINA Mathematics、《统计研究》、Statistics and Computing、Journal of Complexity等学术期刊。

摘要：In the presence of covariates affected by measurement errors, we first propose the corrected least product relative error score (CLAPRES) function to mitigate the effects of measurement errors on parameter estimation in multiplicative regression models. This method is invariant under scale transformations of the positive response and the covariates. To address the challenge of massive datasets with measurement errors, we explore an optimal subsampling algorithm based on the CLAPRES method and derive the optimal subsampling probabilities under the A- and L-optimality criteria. The consistency and asymptotic normality of the subsampling CLAPRES estimators are established. Numerical studies demonstrate the effectiveness of the CLAPRES method.