应amjs澳金沙门线路首页邀请，中国人民大学朱利平教授、浙江大学张立新教授、华东师范大学於州教授、北京师范大学郭旭教授和西安交通大学朱学虎教授等将于2024年8月19日-21日访问amjs澳金沙门线路首页，期间举办专题讨论，欢迎全校师生参加。

报告时间：2024年8月20日(星期二) 08:30开始

报告地点：理工楼631

报告1：SLICED INDEPENDENCE TEST

报告人：朱利平教授

摘要：An ideal independence test should possess three properties: it should be zero-independence equivalent, numerically efficient, and asymptotically normal. We introduce a slicing procedure for estimating a popular measure of nonlinear dependence, leading to the resultant sliced independence test simultaneously possessing all three properties. In addition, the power performance of the sliced independence test improves as the number of observations within each slice increases. The popular rank test corresponds to a special case of the sliced independence test that contains two observations within each slice. The sliced independence test is thus more powerful than the rank test. The size performance of the sliced independence test is insensitive to the number of slices, in that the slicing estimation is consistent and asymptotically normal for a wide range of slice numbers. We further adapt the sliced independence test to account for the presence of multivariate control variables. The theoretical properties are confirmed using comprehensive simulations and an application to an astronomical data set.

报告人简介：朱利平，中国人民大学长聘教授、博士生导师，学校和理工学部学术委员会委员，统计与大数据研究院院长，人民教育出版社普通高中教科书《数学》联合主编，国家重大人才工程入选者，国家杰出青年科学基金获得者，国家重点研发计划首席科学家，兼任中国现场统计研究会生存分析分会理事长和高维数据统计分会副理事长等。先后受邀担任国际统计学领域顶级学术期刊《统计年刊》、国际权威学术期刊《中华统计学》和《多元分析》等副主编，以及国内统计学领域顶级学术期刊《中国科学·数学》（中、英文版）、《系统科学与数学》（中、英文版）和《应用概率统计》等青年编委、编委和副主编等。长期从事大数据统计学基础理论、方法和应用研究。1.在高维度大数据领域，提出不依赖于切片数的累积切片估计方法、不依赖于分布条件的半参数降维方法和不依赖于模型的变量筛选方法，解决了充分降维领域“公开问题”，被认为是该领域“突破性进展”，被列为变量筛选领域“基准方法”。2.在非线性大数据领域，提出投影相关系数度量非线性相关关系，广泛应用于类脑科学和天文学等研究中；原创性提出区间分位数相依基本思想，扩宽了（分布）独立基本概念并建立了（分布）独立与分位数独立和均值独立的联系。3.在大数据应用领域，主持开发的虚假诉讼预警甄别系统已经在四川省高级人民法院和成都市中级人民法院等10家法院部署应用示范，参与编写的人民法院信息化标准《民事案件信息技术规范》已被最高人民法院发布实施。

报告2：Response-Adaptive Randomization in Clinical Trials

报告人：张立新教授

摘要：In clinical trial studies, adaptive randomization is a popular method to randomize patients to treatments. Response-adaptive designs are adaptive schemes to randomize treatments to patients with allocation probabilities depending on the results of previous assignments and treatment outcomes. The adoption of response-adaptive designs has proved to be beneficial to researchers, by providing more efficient clinical trials, and to patients, by increasing the likelihood of receiving better treatment. In this talk, we discuss the several classes of response-adaptive randomization procedures in the view of asymptotic statistical efficiency.

报告人简介：浙江工商大学特聘教授、校学术委员会委员，浙江大学求是特聘教授。1995年获复旦大学理学博士学位，1997年晋升为教授，2001年起先后担任浙江大学统计学研究所副所长、常务副所长、所长，浙江大学数学系副主任、数学科学学院副院长。现任浙江大学数据科学研究中心副主任、中国现场统计研究会常务理事、浙江省现场统计研究会理事长。主要从事临床试验自适应随机化设计、概率极限理论、相依数据模型等领域的研究，发表了学术论文180余篇，先后主持国家自然科学基金面上项目5项、杰出青年基金项目1项、重点项目1项、联合基金重点项目一项，于2008年入选教育部“新世纪优秀人才支持计划”，2018年或2016-2018浙江省“三育人”先进个人和浙江大学第九届“三育人”标兵，2019年入选浙江省科技创新领军人才，2020年当选Institute of Mathematical Statistics Fellow。

报告3：Random Forests and Deep Neural Networks for Euclidean and Non-Euclidean regression

报告人：於州教授

摘要：Neural networks and random forests are popular and promising tools for machine learning. We explore the proper integration of these two approaches for nonparametric regression to improve the performance of a single approach. It naturally synthesizes the local relation adaptivity of random forests and the strong global approximation ability of neural networks. By utilizing advanced U-process theory and an appropriate network structure, we obtain the minimax convergence rate for the estimator. Moreover, we propose the novel random forest weighted local Frechet regression paradigm for regression with Non-Euclidean responses. We establish the consistency, rate of convergence, and asymptotic normality for the Non-Euclidean random forests based estimator.

报告人简介：於州，华东师范大学教授、博士生导师，统计学院副院长。主要研究方向为高维数据统计分析及统计机器学习，在Annals of Statistics，Biiometrika，JASA，JRSSB，Journal of Machine Learning Research，IEEE Information Theory等知名统计及机器学习期刊上发表论文50余篇。曾主持国家重点研发计划课题、自然科学基金青年、面上项目，获得上海市自然科学二等奖等奖项，霍英东青年科学奖二等奖。并先后入选上海高校东方学者特聘教授，上海市青年拔尖人才，上海市青年科技启明星及国家青年人才计划。

报告4：Model-free Variable Importance Testing with Machine Learning Methods

报告人：郭旭教授

摘要：In this paper, we investigate variable importance testing problem in a model-free framework. Some remarkable procedures are developed recently. Despite their success, existing procedures suffer from a significant limitation, that is, they generally require larger training sample and do not have the fastest possible convergence rate under alternative hypothesis. In this paper, we propose a new procedure to test variable importance. Flexible machine learning methods are adopted to estimate unknown functions. Under null hypothesis, our proposed test statistic converges to standard chi-squared distribution. While under local alternative hypotheses, it converges to non-central chi-square distribution. It has non-trivial power against the local alternative hypothesis which converges to the null at the fastest possible rate. We also extend our procedure to test conditional independence. Asymptotic properties are also developed. Numerical studies and two real data examples are conducted to illustrate the performance of our proposed test statistic.

报告人简介：郭旭博士，现为北京师范大学统计学院教授，博士生导师。郭老师一直从事回归分析中复杂假设检验的理论方法及应用研究，近年来旨在对高维数据发展适当有效的检验方法。部分成果发表在JRSSB，JASA，Biometrika和JOE。现主持国家自然科学基金优秀青年基金。曾荣获北师大第十一届“最受本科生欢迎的十佳教师”，北师大第十八届青教赛一等奖和北京市第十三届青教赛三等奖。

报告5：Corrected kernel principal component analysis for model structural change detection

报告人：朱学虎教授

摘要：This paper develops a method to detect model structural changes by applying a Corrected Kernel Principal Component Analysis (CKPCA) to construct the so-called central distribution deviation subspaces. This approach can efficiently identify the distribution changes in these dimension reduction subspaces. We derive that the locations and number changes in the dimension reduction data subspaces are identical to those in the original data spaces. Meanwhile, we also explain the necessity of using CKPCA as the classical KPCA fails to identify the central distribution deviation subspaces in these problems. Additionally, we extend this approach to clustering by embedding the original data with nonlinear lower dimensional spaces, providing enhanced capabilities for clustering analysis. The numerical studies on synthetic and real data sets suggest that the dimension reduction versions of existing methods for change point detection and clustering significantly improve the performances of existing approaches in finite sample scenarios.

报告人简介：朱学虎，博士，西安交通大学教授，博士生导师。目前主要从事统计学习、高维数据分析、隐私保护等领域的基础理论与应用研究。在国际权威期刊JASA、JBES、IEEE TGRS以及计算机顶会NeurIPS等发表论文30余篇，先后主持国家自然科学基金面上项目和青年项目、国家社会科学基金项目等；作为骨干成员参与科技部重点研发项目、国家自然科学基金重点项目等；入选2022陕西省高校青年杰出人才支持计划、2021年仲英青年学者等。

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