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20210107HaiYing Wang:Maximum sampled conditional likelihood estimation for informative subsample
时间:2021-01-05

报告时间:202117日(周四)上午 9:00

报告形式:腾讯会议

报告嘉宾:HaiYing Wang

报告题目:Maximum sampled conditional likelihood estimation for informative  subsample


报告摘要

Subsampling is an effective approach to extract useful information from massive data sets when computing resources are limited. Existing investigations focus on developing better sampling procedures and deriving probabilities with higher estimation efficiency. After a subsample is taken from the full data, most available methods use an inverse probability weighted target function to define the estimator. This type of weighted estimator reduces the contributions of more informative data points, and thus it does not fully utilize information in the selected subsample. This paper focuses on parameter estimation with selected subsample, and proposes to use the maximum sampled conditional likelihood estimator (MSCLE) based on the sampled data. We established the asymptotic normality of the MSCLE, and prove that its variance covariance matrix reaches the lower bound of asymptotically unbiased estimators. Specifically, the MSCLE has a higher estimation efficiency than the weighted estimator. We further discuss the asymptotic results with the L-optimal subsampling probabilities, and illustrate the estimation procedure with generalized linear models. Numerical experiments are provided to evaluate the practical performance of the proposed method.


报告人简介

 HaiYing Wang is an Assistant Professor in the Department of Statistics at the University of Connecticut. He was an Assistant Professor in the Department of Mathematics and Statistics at the University of New Hampshire from 2013 to 2017. 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.


主持人简介

许王莉,中国人民大学统计学院教授、应用统计科学研究中心研究员。近年来一直从事模型拟合优度检验,高维数据分析,随机缺失数据,两阶段抽样数据以及纵向数据分析等方面的统计推断研究。先后主持4项国家自然科学基金,1项教育部人文社会科学重点研究基地重大项目和教育部人文社科基金等多项科研课题, 在统计学国际一流期刊(包括顶尖期刊)发表论文60 余篇,并在科学出版社合作出版《非参数蒙特卡洛检验及其应用》和单著《缺失数据的模型检验及其应用》。


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