报告题目: Inference for ultra-high dimensional quasi-likelihood models based on data splitting
报告人：蒋建成教授（美国北卡大学夏洛特分校）
报告时间：2023年6月9日上午10：30-12：00
报告地点：beat365中文官方网站会议室80602
报告摘要：In this article, we develop a valid framework for inference of ultra-high dimensional quasi-likelihood models, based on a novel weighted estimation approach and a data splitting technique. The optimal weight is obtained by maximizing the efficiency of the estimator. Using the weighted estimator, we construct confidence intervals for some group components of the regression coefficient vector and perform the Wald test for a linear structure of the group components. Theoretically, we establish asymptotic normality of the weighted estimator, and asymptotic distributions of the Wald statistic under the null and alternative hypotheses, without assuming model selection consistency. We highlight the advantages of the proposed test over some competitive tests through theoretical and empirical comparisons, which demonstrates the local optimality of the proposed test. Furthermore, we prove that when variable selection consistency is achieved, the proposed Wald test is asymptotically equivalent to the oracle test which knows the true model. The superior finite sample performance of our proposed test is demonstrated via extensive simulations. Finally, we use a breast cancer dataset to illustrate the use of our methodology.
报告人简介：蒋建成，美国北卡大学夏洛特分校数学与统计系教授。主要从事生物统计、金融计量经济学、非参数统计、数据科学等方面的研究，在Annals of Statistics, Biometrika, Journal of American Statistical Association, Journal of the Royal Statistical Society 等国际著名统计期刊发表论文50余篇, 担任Statistica Sinica 等杂志副主编。(Dr. Jiancheng Jiang is a professor of statistics at the University of North Carolina at Charlotte, USA. He was appointed as chair professor of Nankai University in 2017-2020 and served as the statistics program coordinator at UNC Charlotte and the associate editor of Statistica Sinica and other journals since 2017. He has been awarded several NSF/NIH grants since 2004. His research interest ranges from (bio)statistics to econometrics and data science.)
