Feature Screening with Latent Responses
报告人：Cui Hengjian（Capital Normal University）
时间：2022-11-17 10:00 - 11:00
地点： Tencent Meeting ID（450-606-140）
Abstract: A novel feature screening method is proposed to examine the correlation between latent responses and potential predictors in ultrahigh dimensional data analysis. First, a confirmatory factor analysis (CFA) model is used to characterize latent responses through multiple observed variables. The expectation-maximization algorithm is employed to estimate the parameters in the CFA model. Second, R-Vector (RV) correlation is used to measure the dependence between the multivariate latent responses and covariates of interest. Third, a feature screening procedure is proposed on the basis of an unbiased estimator of the RV coefficient. The sure screening property of the proposed screening procedure is established under certain mild conditions. Monte Carlo simulations are conducted to assess the finite sample performance of the feature screening procedure. The proposed method is applied to an investigation of the relationship between psychological well-being and the human genome.
About the Speaker:
崔恒建, 中国科公司系统科学研究所博士毕业，曾任国务院学位委员会学科评议组专家，现为首都师范大学教授，博士生导师，中国科协第十届全委会委员。崔教授在大数据统计建模、高维统计及其稳健统计理论和方法、统计机器学习、金融统计、以及质量管理等领域取得过许多重要的研究成果，发表论文180余篇，其中包括发表在国际顶级的统计和计量经济学杂志JASA、AoS、JRSS(B)、Biometrika和JoE上。此外，崔教授还主持国家自然科学基金重点项目、杰青（B）项目以及多项面上项目、主要参加教育部重大科研基金项目、科技部863等项目。崔教授现担任《数学学报》和《应用数学学报》中、英文版以及《Statistical Theory and Related Fields》编委，中国现场统计研究会副理事长，全国工业统计教育研究会副理事长，北京应用统计学会会长，国际数理统计学会（中国分会）常务理事。崔教授曾获得教育部高等学校科学技术奖-自然科学奖二等奖；全国统计科学研究优秀成果奖一等奖等。
Tencent Meeting（ ID: 450-606-140）
Meeting Link: https://meeting.tencent.com/dm/D4G2RxDrCc1l
