Southwest Jiaotong University School of Mathematics


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来源:   作者:黄磊     日期:2019-05-06 08:59:52   点击数:  

报告题目: Entropy Learning for Dynamic Treatment Regimes

人: 蒋滨雁教授

报告地点: 二教X2511会议室

报告时间: 20190509日,星期四,上午10:30

报告摘要: Estimating optimal individualized treatment rules (ITRs) in single- or multi-stage clinical trials is a key element of personalized medicine and, as a result, is receiving increasing attention within the statistical community. Recent works have suggested that machine learning approaches can provide significantly better estimations than those of model-based methods. However, a proper inference for estimated ITRs has not been well established for machine learning-based approaches. In this paper, we propose an entropy learning approach for estimating optimal ITRs. We obtain the asymptotic distributions for the estimated rules in order to provide a valid inference. The proposed approach is demonstrated to perform well through extensive simulation studies. Finally, we analyze data from a multi-stage clinical trial for depression patients. Our results offer novel findings not revealed by existing approaches.

主讲人简介:蒋滨雁教授,2007年本科毕业于中国科学技术大学,2012年于新加坡国立大学获得统计学博士学位,随后在美国卡内基梅隆大学做博士后研究学者。现任香港理工大学应用数学系助理教授,其丰硕的研究成果多发表在Biometrika, Journal of the American Statistical Association, Computational Statistics and Data Analysis, Statistica Sinica, Journal of Machine Learning Research, Annals of the Institute of Statistical Mathematics, Lifetime Data Analysis, Electronic Journal of Statistics等统计学顶级或高质量期刊上。主要研究方向是复杂数据统计分析,生物医学统计,机器学习等。