时间：2022年5月13日，星期五，上午
报告一(9:00---9:45)
题目：Multiple Structural Break Estimations for Linear Regression with Dependent Observations
摘要：Linear model with multiple change-point appears in a vast amount of statistical and econometric applications. In this article, the change-point detection problem is transformed into a variable selection problem by segmenting the data series and establishing a high-dimensional linear regression model with dependent observations. We apply Group Orthogonal Greedy Algorithm (GOGA) in variable selection to adapt to the situation that the number of change-point increases with the number of observations, adding high-dimensional information criteria (HDIC) to prevent overfitting. In the first step, GOGA+HDIC+Trim is applied to the segmented data for variable selection, significantly reducing the calculation cost. The quasi-likelihood ratio test in the second step can obtain a more accurate changepoint position. Under mild conditions, we prove the consistency of the number and the location of change-point. The simulation results and real data application also demonstrate the effectiveness of the algorithm.
报告人简介：金百锁，中国科学技术大学管理学院统计与金融系教授。2001年毕业于中国科学技术大学获得学士学位，2006年获得中国科学技术大学博士学位。研究方向为变结构模型，随机矩阵，空间统计等。在PNAS，AoS，Biometrika，AAP等期刊已发表学术论文近50篇。先后主持国家自然科学基金青年项目、面上项目、国际交流项目和安徽省自然科学基金杰青项目。现为中国现场统计研究会理事，全国工业统计学教学研究会理事，中国现场统计研究会资源与环境统计分会理事，中国现场统计研究会旅游大数据分会常务理事、副理事长。个人主页：http://bs.ustc.edu.cn/Chinese/Profile-91.html
报告二 (9:50---10:35)
题目：Optimal Parameter-Transfer Learning by Model Averaging under Semiparametric Models
摘要：Transfer learning has attracted more and more attention in the field of artificial intelligence, of which the aim is to improve one target task of interest by utilizing tasks from several related source domains. In this article, we focus on the prediction for semiparametric additive linear model under the setting of transfer learning. Inheriting the spirits of parameter-transfer learning, we assume existing common knowledge shared in parametric components among different models in our framework that is possibly helpful for the target predictive task. We adopt a frequentist model averaging strategy to utilize parameter information. The theoretical properties have also been established, including the asymptotic optimality based on out-of-sample prediction risk and the property of weight convergence under some regularity conditions. Extensive numerical results demonstrate the superiority of the proposed method under various simulation designs comparing with competitive methods. (Jointly with Xiaonan Hu).
报告人简介：张新雨，中科院数学与系统科学研究院预测中心研究员，中科大管理学院双聘教授。主要从事计量经济学和统计学的理论和应用研究工作，具体研究方向包括模型平均、机器学习和组合预测等。担任期刊《JSSC》领域主编、期刊《系统科学与数学》、《数理统计与管理》等的编委，是双法学会数据科学分会副理事长、国际统计学会当选会员，先后主持自科优秀和杰出青年基金项目。个人主页：https://bs.ustc.edu.cn/Chinese/profile-578.html
报告三 (10:40---11:25)
题目：Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix
摘要：The sparse factorization of a large matrix is fundamental in modern statistical learning. In particular, the sparse singular value decomposition and its variants have been utilized in multivariate regression, factor analysis, biclustering, vector time series modeling, among others. The appeal of this factorization is owing to its power in discovering a highly-interpretable latent association network, either between samples and variables or between responses and predictors. However, many existing methods are either ad hoc without a general performance guarantee, or are computationally intensive, rendering them unsuitable for large-scale studies. We formulate the statistical problem as a sparse factor regression and tackle it with a divide-and-conquer approach. In the first stage of division, we consider both sequential and parallel approaches for simplifying the task into a set of co-sparse unit-rank estimation (CURE) problems, and establish the statistical underpinnings of these commonly-adopted and yet poorly understood deflation methods. In the second stage of division, we innovate a contended stagewise learning technique, consisting of a sequence of simple incremental updates, to efficiently trace out the whole solution paths of CURE. Our algorithm has a much lower computational complexity than alternating convex search, and the choice of the step size enables a flexible and principled tradeoff between statistical accuracy and computational efficiency. Our work is among the first to enable stagewise learning for non-convex problems, and the idea can be applicable in many multi-convex problems. Extensive simulation studies and an application in genetics demonstrate the effectiveness and scalability of our approach.
报告人简介：郑泽敏，现为中国科学技术大学管理学院教授、统计与金融系主任、博士生导师，其研究方向是高维统计推断和大数据问题。郑泽敏博士在横跨这一领域的若干关键研究课题上取得了富有创造性的研究成果，研究成果发表在Journal of the Royal Statistical Society: Series B (JRSSB )、Annals of Statistics (AOS)、Operations Research(OR)、Journal of Machine LearningResearch(JMLR)、Journal of Business & Economic Statistics (JBES)等国际统计学、机器学习、计量经济学及管理优化领域的顶级期刊上，曾获南加州大学授予的优秀科研奖和美国数理统计协会颁发的科研新人奖,并于2017年入选中组部青年创新人才计划。个人主页：http://bs.ustc.edu.cn/chinese/profile-302.html
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