学术报告

题      目：Subsampling for Big Data Linear Model with Measurement Errors

报  告  人：王明秋  教授  (邀请人：吴琴 )

                                    曲阜师范大学

时      间：3月22日  11：15-12：00

地     点：数科院西楼中心会议室

报告人简介:

        曲阜师范大学统计与数据科学学院，博士，教授, 博士生导师。主要研究方向高维数据分析，大数据子抽样。中国现场统计研究会统计调查分会常务理事、山东省应用统计学会常务理事、试验设计分会理事、数据科学与人工智能分会理事。先后主持国家自然科学基金面上项目、青年基金和多项省部级基金。

摘      要：

        Subsampling algorithms for various parametric regression models with massive data have been extensively investigated in recent years. However, all existing studies on subsampling heavily rely on clean massive data. In practical applications, the observed covariates may suffer from inaccuracies due to measurement errors. To address the challenge of large datasets with measurement errors, this study explores two subsampling algorithms based on the corrected likelihood approach: the optimal subsampling algorithm utilizing inverse probability weighting and the perturbation subsampling algorithm employing random weighting assuming a perfectly known distribution. Theoretical properties for both algorithms are provided. Numerical simulations and two real-world examples demonstrate the effectiveness of these proposed methods compared to other uncorrected algorithms.

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