報(bào)告人：邱宇謀 教授

報(bào)告題目：Generalized entropy calibration for selection bias

報(bào)告時(shí)間：2026年3月24日（周二）16:00-17:00

報(bào)告地點(diǎn)：云龍校區(qū)6號(hào)樓304報(bào)告廳

主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院、數(shù)學(xué)研究院、科學(xué)技術(shù)研究院

報(bào)告人簡(jiǎn)介：

邱宇謀，博士畢業(yè)于愛荷華州立大學(xué)，后在愛荷華州立大學(xué)統(tǒng)計(jì)系任教。于2023年加入北京大學(xué)數(shù)學(xué)科學(xué)學(xué)院、統(tǒng)計(jì)科學(xué)中心。他的研究包括：高維數(shù)據(jù)分析、高維協(xié)方差矩陣和精度矩陣的統(tǒng)計(jì)推斷、因果分析、缺失數(shù)據(jù)分析。同時(shí)，他也致力于統(tǒng)計(jì)方法在海洋科學(xué)、精準(zhǔn)農(nóng)業(yè)、流行病模型、法醫(yī)學(xué)等領(lǐng)域的應(yīng)用研究。

報(bào)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.