统计学系系列讲座之320-322期

统计学系系列讲座之320期

时间：2018年10月15日（周一）13:30-14:30

地点：史带楼502室

主持人：张新生 教授 复旦大学管理学院统计学系

主题：Functional Regression for Brain Imaging

主讲人：Professor Bin Nan Department of Statistics, University of California at Irvine

简介：Bin Nan 教授是美国统计学会（ASA）和国际数理统计学会（IMS）的Fellow、以及国际统计研究会（ISI）Elected Member。目前担任统计期刊Statistics in Biosciences 和 Lifetime Data Analysis的Associate Editor。在JASA，AOS，AOAS，Biometrika等国际期刊上发表论文超过100篇，他的研究兴趣主要集中在生存分析、高维大脑图像的数据分析、纵向数据的变点分析等研究领域。 

摘要：It is well-known that the major challenges in analyzing imaging data arise from spatial correlation and high-dimensionality of voxels. Our primary motivation and application come from brain imaging studies on cognitive impairment in elderly subjects with brain disorders. We propose an efficient regularized Haar wavelet-based approach for the analysis of three-dimensional brain image data in the framework of functional data analysis, which automatically takes into account the spatial information among neighboring voxels. We conduct extensive simulation studies to evaluate the prediction performance of the proposed approach and its ability to identify related regions to response variable, with the underlying assumption that only few relatively small subregions are associated with the response variable.We then apply the proposed method to searching for brain subregions that are associated with cognition using PET images of patients with Alzheimer's disease, patients with mild cognitive impairment, and normal controls. Additional challenges, current and future directions of statistical methods in imaging analysis of AD will also be iscussed.

统计学系系列讲座之321期

时间：2018年10月15日（周一）14:30-15:00

地点：史带楼502室

主持人：沈娟 博士 复旦大学管理学院统计学系

主题：Correlation Tensor Decomposition and Its Application in Spatial Imaging Data

主讲人：Yujia Deng, Ph.D Student  University of Illinois at Urbana-Champaign

摘要：In this talk, we propose a new method to analyze spatial-correlated imaging data. In contrast to the conventional multivariate analysis where the variables are treated as vectors and correlation is represented as a matrix form, we formulate a tensor structure to incorporate the spatial correlation information. Specifically, we propose an innovative algorithm to decompose the spatial correlation into a sum of rank-1 tensor and an identity core tensor such that the structure of the spatial information can be captured effectively and the dimension of original data size can be reduced simultaneously. We show that the proposed method can identify the block patterns of spatial correlations of imaging data effectively and efficiently. In addition, we apply the proposed method to the early-stage breast cancer image data to detect the microvescicles and compare the classification result with the Convolutional Neural Network (CNN). This is joint work with Professor Annie Qu.
 

统计学系系列讲座之322期

时间：2018年10月15日（周一）15:00-15:30

地点：史带楼502室

主持人：沈娟 博士 复旦大学管理学院统计学系

主题：Network community detection with dependent connectivity

主讲人：Yubai Yuan, Ph.D student University of Illinois at Urbana-Champaign

摘要：In network analysis, it is common that within community is more likely connected than between community, which is reflected by the edges within a community are more correlated. However, the traditional probabilistic models for community detection like stochastic block model (SBM) are not able to capture the dependence among edges. The revised SBM based on random effects can only handle exchangeable dependence structure on whole networks. In this talk, we propose a new community detection approach based on the Bahadur representation to utilize the within-community dependence of connectivity. The proposed method allows for the heterogeneity among edges and provides greater flexibility in handling different types of within-community dependence structure. In addition, the proposed algorithm does not involve specifying the likelihood function that could be intractable when correlations exist among edges. We demonstrate the application of the proposed method to the agricultural product trading networks. This is joint work with Professor Annie Qu.

                                                           统计学系 

2018-9-30