报告题目：Proper inference for value function in high-dimensional Q-learning for dynamic treatment regimes
报 告 人：朱文圣，东北师范大学
报告时间：2019年3月17日（星期日）上午10:30
报告地点：东校区机关楼316会议室
报告人简介：
朱文圣，东北师范大学YABOCOM·(中国)官方网站教授、博士生导师、副院长。2006年12月博士毕业于东北师范大学，2013年12月起任东北师范大学YABOCOM·(中国)官方网站教授。2008-2010年在耶鲁大学做博士后研究，2015-2017年访问北卡大学教堂山分校。现兼任中国现场统计研究会计算统计分会副理事长，吉林省现场统计研究会秘书长，美国数学会 mathreview 评论员。主要从事统计学的方法与应用研究，研究方向为生物统计学和生物信息学。在统计学国际顶级期刊Journal of the American Statistical Association (JASA)、医学图像著名期刊Neuro- Image、生物信息学著名期刊BMC Bioinformatics、统计遗传学著名期刊Genetic Epidemiology等发表学术论文多篇。主持并完成国家自然科学基金面上项目、青年项目、教育部留学回国人员科研启动项目、吉林省自然科学基金多项，现正在主持国家自然科学面上基金一项。
报告简介：
Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients' responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of non-regularity problems in the presence of non-respondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high. In this paper, we propose a high-dimensional Q-learning (HQ-learning) to facilitate the inference of optimal values and parameters. The proposed method allows us to simultaneously estimate the optimal dynamic treatment regimes and select the important variables that truly contribute to the individual reward. At the same time, hard thresholding is introduced in the method to eliminate the effects of the non-respondents. The asymptotic properties for the parameter estimators as well as the estimated optimal value function are then established by adjusting the bias due to thresholding. Both simulation studies and real data analysis demonstrate satisfactory performance for obtaining the proper inference
for the value function for the optimal dynamic treatment regimes.
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