报告题目：Optimal response-free cluster subsampling for longitudinal data under measurement constraints
时 间：2026年11月16日（星期一）9：00
地 点：科研楼18号楼1102
主 办：数学与统计学院、分析数学及应用教育部重点实验室、统计学与人工智能福建省高校重点实验室
参加对象：感兴趣的老师和学生
报告摘要：Under measurement constraints, where covariates are always accessible but obtaining responses is costly or restricted, we propose a unified response-free cluster subsampling framework for massive longitudinal data, focusing on two aspects. First, when the dimension of covariates is fixed and small, to account for within-subject correlation, we consider cluster subsampling and formulate a response-free weighted quasi-score to obtain the subsample estimator with consistency and asymptotic normality. An optimal cluster subsampling scheme is obtained by optimizing a general criterion that encompasses both A-optimality andL-optimality criteria. To enhance the estimation efficiency, a response-free unweighted estimator is subsequently constructed based on the optimal subsample and a two-step algorithm is devised to facilitate practical implementation. Second, when the dimension of covariates is comparable to or exceeds the subsample size, we further construct a response-free weighted quasi decorrelated score for the preconceived low-dimensional parameter of main interest and derive the optimal subsampling schemes. The resulting unweighted estimator and a two-step algorithm are also proposed. Extensive simulation studies, along with a real-data application,are conducted to empirically demonstrate the effectiveness of the proposed methods.
报告人简介：王磊，南开大学统计与数据科学学院教授、博导、百名青年学科带头人。研究方向为统计学习和复杂数据分析，已在统计学期刊Biometrika、JMLR、IEEE TIT、AOAS、Bernoulli、JCGS、Statistica Sinica等发表学术论文多篇，主持3项国家自然科学基金和1项天津市自然科学基金项目。