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学术报告

Title: Rare Events Data and Maximum Sampled Conditional Likelihood

Speaker: Dr. HaiYing Wang Department of Statistics at the University of Connecticut

Abstract：In this talk, we show that the available information about unknown parameters in rare events data is only tied to the relatively small number of cases, which justifies the usage of negative sampling. However, if the negative instances are subsampled to the same level of the positive cases, there is information loss. We derive an optimal sampling probability for the inverse probability weighted (IPW) estimator to minimize the information loss. We further we propose a likelihood-based estimator to further improve the estimation efficiency, and show that the improved estimator has the smallest asymptotic variance.

报告人简介：HaiYing Wang is an Associate Professor in the Department of Statistics at the University of Connecticut. He obtained his Ph.D. from the Department of Statistics at the University of Missouri in 2013, and his M.S. from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences in 2006. His research interests include informative subdata selection for big data, model selection, model averaging, measurement error models, and semi-parametric regression. His research has been published in top statistics and machine learning journals (e.g., Biometrika, IEEE Transactions on Information Theory, JASA, and JMLR) and conferences (e.g., ICML and NeurIPS)

报告时间：2023年6月21日（周三）上午9:00-10:00

报告地址：腾讯会议：361-443-016

联系人: 胡涛