Southwest Jiaotong University School of Mathematics


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美国 Bentley大学翁嘉迎教授学术报告

来源:   作者:     日期:2021-04-06 15:07:13   点击数:  

美国 Bentley大学翁嘉迎教授学术报告

报告时间:202149日(周五)上午 930-1030

报告地点:腾讯会议室, ID659 527 912

主讲人简介:翁嘉迎,美国 Bentley大学数学科学系的助理教授, 2019年在肯塔基大学获得的博士学位。她的研究兴趣是充分降维,变量选择,凸优化,高维统计和统计学习。


Minimum discrepancy function in sufficient dimension reduction


Sufficient dimension reduction aims to reduce the dimension of predictors while maintaining the regression information. Recently, researchers study an impressive range of sparse inverse regression estimators. Nonetheless, conspicuously less attention has been given to the multivariate response with high-dimensional covariates settings. To fill the gap, we investigate Fourier transform inverse regression approach via regularized quadratic discrepancy functions. Theoretically, we establish the consistency and oracle property for the proposed estimators. We propose an iterated alternating direction method of multipliers (ADMM) algorithm to estimate two target parameters simultaneously. We derive the explicit solution for each step of the ADMM algorithm. Numerical studies and real data analysis confirm the theoretical properties and yield superior performance of our proposed methods. In specific, our proposal has higher support recovery rates compared to the state-of-the-art approach. We develop an open-source Python package called ADMMFTIRE accompanying this paper, available online.