报告时间：2021年12月14日

               下午14:00-15:00

报告地点：腾讯会议

              （会议ID：445 501 991）

报告嘉宾：Jeffrey Chu

报告主题：关于减少大数据集大小估计量的偏差

报告摘要

Bias reduction in the population size estimation of large data sets

Large data sets can often be represented in terms of large graphs or networks. The estimation of the population size of these large data sets and hard to reach populations can be a significant problem. In many situations, manpower is limited and the manual processing of large data sets can be time consuming. In addition, accessing the full population of data may be restricted by factors such as time and cost. Four new population size estimators are proposed, as extensions of existing methods, and their performances are compared in terms of bias with two existing methods in the big data literature. These would be particularly beneficial in the context of time-critical decisions or actions. The comparison is based on a simulation study and the application to five real network data sets (Twitter, LiveJournal, Pokec, Youtube, Wikipedia Talk). Whilst no single estimator (out of the four proposed) generates the most accurate estimates overall, the proposed estimators are shown to produce more accurate population size estimates for small sample sizes.

个人简介

Jeffrey Chu，中国人民大学统计学院，数理统计系讲师，英国曼彻斯特大学数学学院博士。曾先后在英国曼彻斯特大学，马德里卡洛斯三世大学作为博士后和讲师承担教学、科研工作。研究方向围绕统计分布理论，统计学在区块链和加密货币的应用。研究论文发表于Journal of Computational and Applied Mathematics，Computational Statistics & Data Analysis等国际高水平期刊上。主持并参与了沙迦美国大学智慧城市研究项目等多个课题。