学术报告一

报告题目: Prediction analysis for microbiome sequencing data

报告人：王涛, 教授、博导(上海交通大学)

报告时间；2018年5月19日上午9:00-11:00

报告地点：星际电子在线学术报告厅（25教1802报告厅）

摘要: One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are over-dispersed with many zeros. To address these challenges, we introduce a new statistical framework, called Predictive Analysis in Metagenomics via Inverse Regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for over-dispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension-reduction structure in the model. An efficient Monte Carlo expectation-maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.

报告人简介：王涛老师2007年毕业于东南大学数学系，2010年在华东师范大学获得硕士学位，2013年在香港浸会大学获得博士学位。目前王涛老师的主要研究兴趣在于高维变量选择与维数下降、空间与时间数据、半参与非参数推断等。王涛老师目前已在Journal of the American Statistical Association、Statistics and Computing、Biometrics、Computational Statistics and Data Analysis、The Annals of Applied Statistics等SCI期刊上发表多篇学术论文。

学术报告二

报告题目：Automatic Detection of Significant Areas for Functional Data with Directional Error Control

报告人：许佩蓉, 副教授 (上海师范大学)

报告时间；2018年5月19日上午9:00-11:00

报告地点：星际电子在线学术报告厅（25教1802报告厅）

摘要： In this paper we propose a large-scale multiple testing procedure to find the significant sub-areas between two samples of curves automatically. The procedure is optimal that controls the directional false discovery rate at any specified level on a continuum asymptotically. By introducing a nonparametric Gaussian process regression model for the two-sided multiple test, the procedure is computationally inexpensive. It can cope with problems with multidimensional covariates and accommodate different sampling designs across the samples. We further propose the significant curve/surface, giving an insight on dynamic significant differences between two curves. Simulation studies demonstrate that the proposed procedure enjoys superior performance with strong power and good directional error control. The procedure is also illustrated with the application to two executive function studies in hemiplegia.

报告人简介：许佩蓉博士2007年毕业于星际电子在线数学系，2013年在华东师范大学获得博士学位，目前主要研究兴趣在于纵向数据分析、变量选择、高维数据分析、分类和聚类分析，目前已在Biometrika、Annals of the Institute of Statistical Mathematics、Bernoulli、Computational Statistics & Data Analysis、Journal of Multivariate Analysis等刊物上发表论文10余篇，主持国家及江苏省青年项目各1项。