2018年12月14日下午3点在林园校区东一号516室，为了加强同学们对数学世界的了解，感悟数学知识的强大，增强同学们的学习兴趣，数学与统计学院荣邀东北师范大学朱文圣教授和俄勒冈州立大学江源莅临我院做学术报告。报告由学院副院长王纯杰主持，学院副院长徐平峰、学院部分老师、研究生、及本科生和其他学院师生参加了本次学术报告会。

报告题目：Proper inference for value function in high-dimensional Q-learning for dynamic treatment regimes

摘要：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.

朱文圣简介：朱文圣，东北师范大学数学与统计学院教授、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数学与统计学院教授。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NeuroImage、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。

报告题目：Microbial network estimation using bias-corrected graphical lasso

摘要：With the increasing availability of microbiome 16S data, network estimation has become a useful approach to studying the interactions between Network estimation on a set of variables is frequently explored using graphical models, in which the relationship between two variables is modeled via their conditional dependency given the other variables. In recent years, various methods for sparse inverse covariance estimation have been proposed to estimate graphical models in the high-dimensional setting, including graphical lasso. However, current methods do not address the compositional count nature of microbiome data, where abundances of microbial taxa are not directly measured, but are reflected by the observed counts in an error-prone manner. Adding to the challenge is that the sum of the counts within each sample, termed “sequencing depth”, is an experimental technicality, which carries no scientific information but can vary drastically across samples. To address these issues, we develop a new approach to network estimation, which models the microbiome data using a multinomial log-normal distribution with the finite sequencing depth explicitly incorporated. We propose to improve the empirical covariance estimator via a computationally simple procedure that corrects the bias arising from the heterogeneity in sequencing depth. We then build our inverse covariance estimator on graphical lasso. We will show the advantage of our method in comparison to current approaches for inverse covariance estimation under a variety of simulation scenarios. We will also illustrate the use of our method in an application to a human microbiome data set.

江源简介：江源，博士，2004年毕业于中国科技大学，获学士学位；2008年毕业于美国威斯康星大学麦迪逊分校，获统计学博士学位。2008年至2011年，在耶鲁公共健康学院从事博士后研究；2011年加入俄勒冈州立大学统计系工作。现为American Statistical Association (ASA)会员，Institute of Mathematical Statistics (IMS)会员，International Chinese Statistical Association (ICSA)会员，International Genetic Epidemiology Society (IGES)会员；主要从事Data integration, Variable selection, Network analysis, Statistical genetics, GWAS等研究，在在统计学国际顶级期刊Journal of the American Statistical Association，Biometrika等发表论文多篇，被引用300多次。

报告会中，朱文圣教授从以一些医学实例引出报告的主题，其中数据基于随机化而来的，研究的是对于不同的人给出不同治疗方案，运用动态治疗方案（一系列决策规则），主要针对的目标给出最优的治疗方案，并对条件期望进行建立线性模型，其关心的是治疗的差别，给出一些定理以及定理证明。之后，在理论的基础上，朱文圣给出了数据的模拟，用以说明理论的应用。最后，朱文圣老师对同学们提出的问题进行了详细的解答，也与学院里的老师进行了深入的互动。

江源老师结合他多年的学习，教学和研究经验，向同学们介绍了有关偏差校正图形lasso的微生物网络估计以及近期研究进展。首先，我们找到一个有关微生物数量的数据，然后用每个微生物的比例进行一种变换，最后用glasso进行图模型建模。做完报告后，数学与统计学院的老师和同学们与江源老师进行了互动，同学们及时提出了自己的疑惑，江源老师也对同学们提出的问题进行了详细的解答，又为同学们在这个方向上的学习给出了指导性的建议。并激励同学们要担负起数学与统计人才应有的使命，为我国的数学与统计学建设作出应有的贡献，也希望大家能够脚踏实地的学习理论知识，为数学与统计学的发展不懈努力。

本次报告会使同学们对数学与统计学中的知识又有了更宽泛的了解，提高了同学们的学习动力及学习积极性。一方面让同学们对统计学的发展前景有了更深刻的认识，另一方面让同学们对自己的未来有了更明确的规划。同时，不仅对外加强了我院的学术交流，而且对内营造了师生们良好的研学氛围，开扩了视野，进一步激发了广大师生的极大兴趣，在场师生均表示，聆听本次报告受益匪浅，获益良多，增强了自信心，进一步争强了广大师生的统计思维，加强了学生对统计专业的喜爱！

数学与统计学院

2018年12月14日