1.时间：2011年11月4日（星期五）下午2点至3点

地点：图书馆小报告厅

报告题目：Functional Boxplots for Visualization of Complex Curve/Image Data: An Application to Precipitation and Climate Model Output.

报告人：Marc G. Genton, Department of Statistics, Texas A&M University

报告内容摘要：

In many statistical experiments, the observations are functions by nature, such as temporal curves or spatial surfaces/images, where the basic unit of information is the entire observed function rather than a string of numbers. For example the temporal evolution of several cells, the intensity of medical images of the brain from MRI, the spatio-temporalrecords of precipitation in the U.S., or the output from climate models, are such complex data structures. Our interest lies in the visualization of such data and the detection of outliers. With this goal in mind, we have defined functional boxplots and surface boxplots. Based on the center outwards ordering induced by band depth for functional data or surface data, the descriptive statistics of such box plots are: the envelope of the 50% central region, the median curve/image and the maximum non-outlying envelope. In addition, outliers can be detected in a functional/surface box plot by the 1.5 times the 50% central region empirical rule, analogous to the rule for classical boxplots. We illustrate the construction of a functional boxplot on a series of sea surface temperatures related to the El Nino phenomenon and its outlier detection performance is explored by simulations. As applications, the functional boxplot is demonstrated on spatio-temporal U.S. precipitation data for nine climatic regions and on climate general circulation model (GCM) output. Further adjustments of the functional boxplot for outlier detection in spatio-temporal data are discussed as well. The talk is based on joint work with Ying Sun.

报告人简介：

Marc G. Genton教授，美国德克萨斯农工大学统计学系教授，瑞士联邦理工学院学士、硕士、博士，空间统计学术带头人，应用数学与计算科学研究所统计学方向带头人，是国际顶级统计学组织ASA、IMS、ISI等组织机构的入选会员，同时，他还荣获了2010年国际顶级统计学组织ASA颁发的杰出成就奖，2010年国际环境社会组织颁发的杰出成就奖。，对诸多研究领域都做出了基础性创新性的贡献，包括空间统计、时间序列、多维非高斯分布、稳健性、数据挖掘、机器学习等方面，在125项已发表的论文或已出版的著作中，充分显示了他深厚的统计学理论功底和积极的统计学应用与实施，尤其是善于将理论分析与实践问题紧密结合起来开展研究工作，如针对野火采集点燃系统的时空模型，研究了风能预测，研究了气温变化与气候变迁等。

2.时间：2011年11月4日（星期五）下午3点30分至4点30分

地点：图书馆小报告厅

报告题目：A Semiparametric Approach to Dimension Reduction

报告人：Yanyuan Ma，Department of Statistics, Texas A&M University

报告内容摘要：

We provide a novel and completely different approach to dimensionreduction problems from the existing literature. We cast the dimension reduction problem in a semiparametric estimation framework and derive estimating equations. Viewing this problem from the new angle allows us to derive a rich class of estimators, and obtain the classical dimension reduction techniques as special cases in this class. The semiparametric approach also reveals that in the inverse regresssion context while keeping the estimation structure intact, the common assumption of linearity and/or constant variance on the covariates can be removed at the cost of performing additional nonparametric regression. The semiparametric estimators without these common assumptions are illustrated through simulation studies and a real data example.This is joint work with Liping Zhu.

报告人简介：

Yanyuan Ma教授，美国德克萨斯农工大学统计学系教授，北京大学数学理学学士、斯坦福大学数学硕士、麻省理工学院应用数学博士，北卡罗来纳州立大学博士后，国际统计学会、国际生物数学统计学会和泛华统计学会会员，在半参数方法、测量误差模型、混合样本问题、潜变量模型、降维技术以及斜球变量分布等统计学领域有显著成就。

3.时间：2011年11月4日（星期五）下午4点30分至5点30分

地点：图书馆小报告厅

报告题目：Model-Free Feature Screening for Ultrahigh Dimensional Data

报告人：朱利平 上海财经大学统计与管网络电玩城

报告内容摘要：

With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this talk, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultra high-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We demonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis. This is a joint work with Professors Li Lexin at NC State, Li Runze at Penn State and Zhu Lixing at HKBU.

报告人简介：

朱利平，华东师范大学博士，上海财经大学统计系副教授，08年和11年国家自然科学基金获得者，2008年: 国家统计局第九届全国统计科研成果奖优秀博士论文二等奖。主要从事生物统计和统计的大规模数据降维技术研究，已在统计四大顶尖杂志上以第一作者发表论文5篇。

欢迎广大师生参加！

网络电玩城

2011年10月31日