报告题目：Optimal subsampling estimation of multiplicative models based on the least squared relative error criterion

报 告 人：刘惠篮副教授（贵州大学）

报告时间：2025年11月8日（星期六）9:10-9:50

报告地点：数学大楼报告厅三（814）

参加人员：教师、研究生、本科生

报告摘要：This paper investigates the optimal subsampling for multiplicative models based on the least squared relative error criterion. Initially, the consistency and asymptotic normality of the estimator from the general subsampling algorithm are demonstrated. Subsequently, the optimal subsampling probability is determined according to the L-optimality criterion. Based on the derived probability, a two-step algorithm is proposed: first, the uniform subsampling probability is used in a general subsampling algorithm to obtain an initial estimation; then, the optimal subsampling probability is calculated based on this initial estimation, and subsequently used for sampling to obtain the final estimation. We also establish the consistency and asymptotic normality of the estimator obtained by the two-step algorithm. Numerical studies and real data analysis confirm the efficiency of the proposed method.

报告人简介：刘惠篮，贵州大学副教授，主要从事复杂数据统计建模领域的研究，并在统计领域期刊上发表SCI论文多篇，主持完成国家自然科学基金项目1项，省部级项目3项，横向课题2项。