统计与数据科学系系列学术报告之三百八十三

时间：11月15日（周二） 14:00-15:00

地点：腾讯会议号：587 358 468，密码：002156

主持人：朱仲义教授

题目：Paradoxes and resolutions for semiparametric fusion of individual and summary data

报告人：苗旺北京大学概率统计系

个人简介：苗旺现为北京大学概率统计系研究员， 2008-2017年在北京大学数学科学学院读本科和博士，2017-2018年在哈佛大学生物统计系做博士后研究，2018年入职北京大学光华管理学院，2020年调入数学科学学院。苗旺的研究兴趣包括因果推断，缺失数据分析，及其在生物统计，流行病学，经济学和人工智能研究中的应用，与合作者提出混杂分析的代理推断方法，发展非随机缺失数据的识别性和双稳健估计理论，以及数据融合的半参数理论。个人网页https://www.math.pku.edu.cn/teachers/mwfy/

摘要：Suppose we have available individual data from an internal study and various types of summary statistics from relevant external studies. External summary statistics have been used as constraints on the internal data distribution, which promised to improve the statistical inference in the internal data; however, the additional use of external summary data may lead to paradoxical results: efficiency loss may occur if the uncertainty of the summary statistics is not negligible and estimation bias can emerge if they are obtained in a different population from the internal study. We investigate these paradoxical results in a semiparametric framework. We establish the semiparametric efficiency bound for estimating a general functional of the internal data distribution, which is shown to be no larger than that using only internal data. We propose a data-fused efficient estimator that achieves this bound so that the efficiency paradox is resolved. This data-fused estimator is further regularized with adaptive lasso penalty so that the resultant estimator can achieve the same asymptotic distribution as the oracle one that uses only unbiased summary statistics, which resolves the bias paradox. Simulations and application to a Helicobacter pylori infection dataset are used to illustrate the proposed methods.