美国威斯康星大学麦迪逊分校张正军教授做客北航经商大讲堂
2019年5月21日，美国威斯康星大学麦迪逊分校统计系长聘正教授、国际顶级期刊Journal of Business& Economic Statistics副主编、Statistica Sinica副主编张正军教授来到北航经济管理学院，做客第30期北航经商大讲堂，他的报告题目是SimulatedDistribution Based Learning for Non-regular and Regular Statistical Inferences。经济管理学院郑海涛副院长主持了本次大讲堂。经管学院师生30余人参加了此次报告会。
报告会上，张教授提出了一种新的统计推断方法，是首次把系统仿真的方法用到了机器学习中；建立了一个基本定理，保证了从随机变量（或误差项）的假设分布转换的次序统计（到给定的边际），任意接近相同边际分布的模拟序列的次序统计。
在报告结束后，张教授与经管学院师生进行了亲切的互动交流，一起探讨报告中所涉及定理的合理性及适用性等问题，张教授报告会在热烈的学术氛围中圆满结束。
张正军教授个人简介：
张正军教授现为美国威斯康星大学麦迪逊分校统计系长聘正教授、美国统计协会会士、国际数理统计协会财务总监、国际顶级期刊Journalof Business & Economic Statistics副主编、Journalof Econometrics金融工程与风险管理特刊共同主编、StatisticaSinica副主编；北卡罗来纳大学教堂山分校统计学博士，北京航空航天大学管理科学与工程博士。主要研究方向包括：金融时间序列分析、极值理论、异常气候分析、稀有疾病（癌症、帕金森综合症、奥兹海默病等等）分析、金融风险的建模和评估、市场系统性风险评估等等。
Prof.
Zhengjun Zhang
：
Simulated Distribution Based Learning for Non-regular and Regular Statistical Inferences
On 21st May, 2019, we welcome Prof. ZhengjunZhang to our series lecture on “Economics and Business”, as well as his speechon Simulated Distribution Based Learning
for Non-regular and Regular Statistical Inferences. Zhengjun Zhang is Professor at Department of Statistics in the University of Wisconsin-Madison, Associate Editor for the
Journal of Business &Economic Statistics and Associate Editor for the Statistica Sinica. He obtained his Ph.D both from University of North Carolina at Chapel Hill and from
Beihang University in Management Science and Engineering. He is interested inresearch on financial time series analysis, extreme value theory, abnormal climate analysis,
rare disease analysis, financial risk modeling and evaluation, market systemic risk assessment and so on. The lecture was hosted by Associate Dean Haitao Zheng from
School of Economics and Management at Beihang University.
In the following lecture, Prof. Zhengjun Zhang establishes a fundamental theorem which guarantees the transformed order statistics (to a given marginal) from the assumed
distribution of a random variable (or an errorterm) to be arbitrarily close to the order statistics of a simulated sequence of the same marginal distribution. Different from the
Kolmogorov-Smirnov test which is based on absolute errors between the empirical distribution and the assumed distribution, the statistics proposed in the paper are based on
relative errors of the transformed order statistics to the simulated ones. Uponusing the constructed statistic (or the pivotal quantity in estimation) as a measure of the relative
distance between two ordered samples, we estimate parameters such that the distance is minimized. Unlike many existing methods,e.g., maximum likelihood estimation,
which rely on some regularity conditionsand/or the explicit form of probability density function, the new method only assumes a mild condition that the cumulative
distribution function can be approximated to a satisfied precision. The paper illustrates simulation examples to show its superior performance. Under the linear regression
settings, the proposed estimation performs exceptionally well regarding preserving the error terms (i.e., the residuals) to be normally distributed which is a fundamental
assumption in the linear regression theory andapplications.
During and after the lecture, more than 30 scholars and students from BUAA SEM discussed the key points of the talk with Prof. Zhengjun Zhang, and also raised quite a few
relevant questions,which provoke further thinking on the issue from both parties.
