報告人：邱宇謀 教授

報告題目：Generalized entropy calibration for selection bias

報告時間：2026年3月24日（周二）16:00-17:00

報告地點：云龍校區6號樓304報告廳

主辦單位：數學與統計學院、數學研究院、科學技術研究院

報告人簡介：

邱宇謀，博士畢業于愛荷華州立大學，后在愛荷華州立大學統計系任教。于2023年加入北京大學數學科學學院、統計科學中心。他的研究包括：高維數據分析、高維協方差矩陣和精度矩陣的統計推斷、因果分析、缺失數據分析。同時，他也致力于統計方法在海洋科學、精準農業、流行病模型、法醫學等領域的應用研究。

報告摘要：

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.