报告人：邱宇谋 教授
报告题目：Generalized entropy calibration for selection bias
报告时间：2026年3月24日（周二）16:00-17:00
报告地点：云龙校区6号楼304报告厅
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
邱宇谋，博士毕业于爱荷华州立大学，后在爱荷华州立大学统计系任教。于2023年加入北京大学数学科学学院、统计科学中心。他的研究包括：高维数据分析、高维协方差矩阵和精度矩阵的统计推断、因果分析、缺失数据分析。同时，他也致力于统计方法在海洋科学、精准农业、流行病模型、法医学等领域的应用研究。
报告摘要：
We propose a unified framework for constructing calibration weights for data with selection bias by maximizing a generalized entropy function subject to carefully chosen calibration constraints. The proposed generalized entropy calibration (GEC) method can be applied to a variety of problems including missing data, causal inference and survey sampling. Compared to widely used augmented inverse propensity weighting (AIPW) methods, the proposed method can integrate information from multiple propensity score and outcome regression models and achieve multiply robust inference under high-dimensional covariates. Traditional calibration methods minimize a distance between calibrated and initial weights. GEC is a novel calibration framework that instead maximizes a generalized entropy function subject to two types of constraints: covariate balancing constraints to incorporate outcome regression models and to improve efficiency and debiasing constraints involving propensity scores. We establish the asymptotic properties of the proposed estimator, including design consistency, asymptotic normality and multiply robustness. Particularly for survey sampling under Poisson design, we develop an optimal entropy function, called contrast-entropy, which minimizes the asymptotic variance among a broad class of entropy functions.
