校庆学术报告（一）
题目：Sufficient Dimension Reduction for Nonignorable Nonresponse
主讲人：王启华，研究员，中国科学院
时间：2019年10月15日(周二) 13:00-13:30
地点：中北校区理科大楼A1716报告厅
摘要：
Sufficient dimension reduction(SDR) for nonignorable nonresponse poses a challenge and thus there is still no article on this problem. In the nonignorable case, methods derived under ignorable missing assumption are invalid and of serious estimation bias, especially when missing rate is high. In this article, a regression calibration based cumulative mean estimation (RC-CUME) procedure is proposed to recover central subspace $\mathcal S_{Y|\mathbf X}$ with the help of a surrogate subspace. Asymptotic properties of RC-CUME are also investigated. To guide practical application, we construct two feasible surrogate subspaces and compare the proposed RC-CUME based on the two surrogate subspaces.
A modified BIC-type criterion is adopted to determine the structural dimension of $\mathcal S_{y|\mathbf X}$. In addition, we extent our procedure to other SDR methods. Simulation studies are carried out to access the finite-sample performances of the proposed RC-CUME approach. A real data analysis is used to illustrate our method.
个人简介：王启华，中国科学院核心骨干特聘研究员，博士生导师，国家杰出青年基金获得者，教育部长江学者奖励计划特聘教授，中科院“百人计划”入选者，首届全国优秀博士论文作者，国际统计研究会当选会员（elected member）, 先后访问加拿大Carleton大学、California大学戴维斯分校、California大学洛杉矶分校、美国Yale大学、美国华盛顿大学、美国西北大学、德国Humboldt大学、澳大利亚国立大学及澳大利亚悉尼大学等。主要从事生存分析、缺失数据分析、高维数据统计分析及非-半参数统计推断等方面的研究。出版专著两部，在 The Annals of Statistics, JASA及Biometrika等国际重要刊物发表论文百余篇，是一些国际与国内刊物的编委。
校庆学术报告（二）
题目：Multiple Change-points Detection in High Dimension
主讲人：王兆军，教授，南开大学统计与数据科学学院
时间：2019年10月15日(周二) 13:30-14:00
地点：中北校区理科大楼A1716报告厅
摘要：
Change-point detection is an integral component of statistical modeling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are typically inapplicable. We propose a novel testing statistic by combining a modified Euclidean distance and an extreme statistic, and its null distribution is asymptotically normal. The new method naturally strikes a balance between the detection abilities for both dense and sparse changes, which gives itself an edge to potentially outperform existing methods. Furthermore, the number of change-points is determined by a new Schwarz’s information criterion together with a pre-screening procedure, and the locations of the change-points can be estimated via the dynamic programming algorithm in conjunction with the intrinsic order structure of the objective function. Under some mild conditions, we show that the new method provides consistent estimation with an almost optimal rate. Simulation studies show that the proposed method has satisfactory performance of identifying multiple change-points in terms of power and estimation accuracy, and two real data examples are used for illustration.
个人简介：王兆军，南开大学统计与数据科学学院执行院长、统计研究院院长、教授、博士生导师，教育部长江学者特聘教授，国务院学位委员会统计学科评议组成员、中国现场统计研究会副理事长、中国现场统计研究会生存分析分会副理事长、中国工业统计学教学研究会副理事长、中国统计教育学会高等教育分会副会长、天津市现场统计研究会理事长、天津市统计学会副会长。曾获全国百篇优博指导教师、教育部全国高校自然科学二等奖及天津市自然科学一等奖。目前为《数理统计与管理》副主编，《数学进展》和《统计信息论坛》编委，研究领域包括工业工程中统计监控与诊断、复杂数据中的变点、异常点检测、实验设计、高维数据统计推断等。目前主持国家自然科学基金重点项目和面上项目各一项，已完成多项国家面上项目。已在Journal of the American Statistical Association、Annals of Statistics、Biometrika、Statistica Sinica、Technometrics等专业顶级期刊上发表数十篇专业学术论文。
校庆学术报告（三）
题目：A Global Bias-Correction DC Method for Biased Estimation under Memory Constraint
主讲人：林路，教授，山东大学金融研究院
时间：2019年10月15日(周二) 14:00-14:30
地点：中北校区理科大楼A1716报告厅
摘要：
This paper establishes a global bias-correction divide-and-conquer (GBC-DC) rule for biased estimation under the case of memory constraint. In order to introduce the new estimation, a closed representation of the local estimators is adopted, aiming to formulate a pro forma linear regression between the local estimators and the true parameter of interest. Least square method is then used within this framework to composite a global estimator of the parameter. Thus, the main advantage over the classical DC method is that the new GBC-DC method can absorb the information hidden in the statistical structure and the variables in each batch of data. Consequently, the resulting global estimator is strictly unbiased or sufficiently bias-corrected even if the local estimator has a non-negligible bias. Moreover, the global estimator is consistent, and even can achieve root-$n$ consistency, without any constraint (or with a weaker constraint) on the number of batches. Another attractive feature of the new method is the computational simplicity and efficiency, without use of any iterative algorithm and local bias-correction. Generally, the proposed GBC-DC method applies to various biased estimations such as regularization-based estimation and nonparametric regression estimation. Detailed simulation studies demonstrate that the proposed GBC-DC approach is significantly bias-corrected, and the behavior is comparable with the full data estimation and is much better than the competitors.
个人简介：林路，山东大学金融研究院副院长、教授、博士生导师，主要从事高维统计、非参数和半参数统计以及金融统计等方的研究，在国际统计学、机器学习和相关应用学科顶级期刊Annals of Statistics, Journal of Machine Learning Research, PLoS computational biology和其它重要期刊发表研究论文90余篇；主持过多项国家自然科学基金课题、博士点专项基金课题、山东省自然科学基金重点项目等；获得国家统计局颁发的统计科技进步一、二等奖，山东省优秀教学成果一等奖；是国家973项目、国家创新群体和教育部创新团队的核心成员，教育部应用统计专业硕士教育指导委员会成员，山东省政府参事。