主 题：Composite likelihood estimation in copula models for longitudinal imaging data
主讲人：Prof. Peter XK Song
主持人：林华珍教授
时 间：2015年6月29日下午2：00-3：00
地 点：通博楼B座212会议室
主办单位：统计研究中心 统计学院 科研处
主讲人简介：
Dr. Peter Song is currently Full Professor of Biostatistics in the School of Public Health, University of Michigan, Ann Arbor. He received his PhD degree in Statistics from University of British Columbia, Vancouver in 1996.Prior to the appointment of professorship at University of Michigan in Ann Arbor,he served as faculty in University of Waterloo and York University in Canada. Dr. Song is an elected member of International Statistical Institute, and the recipient of John von Neumann award from Technical University of Munich, Germany in 2013.He is now Associate Editor of Statistica Sinica, Canadian Journal of Statistics, Sankhya, and a Co-Guest Editor of Statistics and Its Interface. His current research is funded by multiple grants from National Science Foundation and National Institute of Health. Dr. Song has published over 80 articles in top-tier statistical and biomedical journals, including a single-authored monograph "Correlated Data Analysis: Modeling, Analytics and Applications" by Springer. Some of his published works have been reported by CNN, the Wall Street Journal and CBS.
内容提要：
Motivated by the needs of analyzing massive longitudinal imaging data, we present an extension of GeoCopula proposed by Bai et al. (2014). This new model, termed as imageCopula, helps us to address multilevel spatial-temporal dependencies arising from longitudinal imaging data. We propose an efficient composite likelihood approach by constructing joint composite estimating equations (JCEE) and develop computationally feasible algorithm to solve the JCEE. We show that the computation is scalable to large-scale imaging data. We conduct several simulation studies to evaluate the performance of the proposed models and estimation methods. We apply the imageCopula to analyze a longitudinal PET data set from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. This is a joint work with Jian Kang from Emory University。
