时 间：2024年4月30日 10:00-11:00
地 点：普陀校区理科大楼A1716
报告人：邱宇谋 北京大学长聘副教授
主持人：张澍一 华东师范大学助理教授
摘 要：
Incorporating the auxiliary information into the survey estimation is a fundamental problem in survey sampling. Calibration weighting is a popular tool for incorporating the auxiliary information. The calibration weighting method of Deville and Sarndal (1992) uses a distance measure between the design weights and the final weights to solve the optimization problem with calibration constraints. This paper introduces a novel framework that leverages generalized entropy as the objective function for optimization, where design weights play a role in the constraints to ensure design consistency, rather than being part of the objective function. This innovative calibration framework is particularly attractive due to its generality and its ability to generate more efficient calibration weights compared to traditional methods based on Deville and Sarndal (1992). Furthermore, we identify the optimal choice of the generalized entropy function that achieves the minimum variance across various choices of the generalized entropy function under the same constraints. Asymptotic properties, such as design consistency and asymptotic normality, are presented rigorously. The results from a limited simulation study are also presented. We demonstrate a real-life application using agricultural survey data collected from Kynetec, Inc.
报告人简介：
邱宇谋，博士毕业于爱荷华州立大学，先后在内布拉斯加林肯大学和爱荷华州立大学任教。于2023年7月加入北京大学数学科学学院、统计科学中心，职位为长聘副教授。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精准农业、流行病模型、法医学等领域的应用研究。