统计学系系列讲座之294-295期

统计学系系列讲座之294期

时间：2018年4月19日（周四）16:30-17:30

地点：史带楼301室

主持人：夏寅 青年研究员 复旦大学管理学院统计学系

主题：Two-sample Functional Linear Models

主讲人：张日权 教授　华东师范大学

简介：张日权，华东师范大学统计学院院长；大数据统计分析中心主任；《应用概率统计》期刊常务副主编；中国现场统计研究会高维数据统计分会副理事长；中国现场统计研究会经济与金融统计分会副理事长；上海市统计学会副会长。2014年获得上海市育才奖；2016年获得上海市自然科学奖三等奖。发表论文130余篇，SCI收录论文90余篇，由科学出版社出版专著1部，教材1部；主持和参与各类项目20余项，其中主持国家自然基金面上项目3项，教育部博士点基金2项，国家统计局重点项目1项；环保部专项子项目2项，民航局项目2两项。主要研究兴趣包括大数据统计分析，金融统计，非/半参数统计，函数型数据，超/高维数据。

摘要：In this report we study two-sample functional linear regression with a scaling transformation of regression functions. We consider estimation for the intercept, the slope function and the scalar parameter based on the functional principal component analysis. We also establish the rates of convergence for the estimator of the slope function, which is shown to be optimal in a minimax sense under certain smoothness assumptions. We further investigate semiparametric efficiency for the estimation of the scalar parameter and hypothesis testing. We also extend the proposed method to sparsely and irregularly sampled functional data and establish the consistency {for the estimators of the scalar and the slope function. We evaluate numerical performance of the proposed methods through simulation studies and illustrate their utility via analysis of an AIDS data set.

统计学系系列讲座之295期

时间：2018年4月27日（周五）10:00-11:00

地点：史带楼502室

主持人：夏寅 青年研究员 复旦大学管理学院统计学系

主题：Methods for High Dimensional Compositional Data Analysis in Microbiome Studies

主讲人：李洪哲  教授　美国宾夕法尼亚大学

简介：李洪哲教授是美国宾夕法尼亚大学佩雷尔曼医学院生物统计学和统计学教授，生物统计系研究生项目主席，大数据统计中心主任。李洪哲教授是美国统计协会（American Statistical Association）会士、数理统计学会（Institute of Mathematical Statistics）会士、美国科学促进会（American Association for the Advancement of science）会士。 李洪哲教授的研究成果多发表在Science, Nature, Nature Genetics, Science Translational Medicine, JASA, JRSSB, Annals of Statistics, Biometrika等国际顶级期刊上。

摘要：Human microbiome studies using high throughput DNA sequencing generate compositional data with the absolute abundances of microbes not recoverable from sequence data alone. In compositional data analysis, each sample consists of proportions of various organisms with a unit sum constraint. This simple feature can lead traditional statistical methods when naively applied to produce errant results and spurious associations. In addition, microbiome sequence data sets are typically high dimensional, with the number of taxa much greater than the number of samples. These important features require further development of methods for analysis of high dimensional compositional data.  This talk presents several latest developments in this area, including methods for estimating the compositions based on sparse count data, two-sample test for compositional vectors and regression analysis with compositional covariates.  Several micobiome studies at Penn are used to illustrate these methods and several open questions will be discussed.

    统计学系 

2018-4-18