主题：Composite likelihood estimation in copula models for panel imaging data
主讲人：Professor Peter XK Song, University of Michigan
主持人：统计学院院长助理 龚金国副教授
时间：2015年11月20日（星期五）上午10：30-11：30
地点：柳林校区 通博楼B座212会议室
主办单位：统计学院 科研处
主讲人简介：
Dr. Song is Professor of Biostatistics at the Department of Biostatistics, School of Public Health in the University of Michigan, Ann Arbor. He received his PhD in Statistics from the University of British Columbia, Vancouver, Canada in 1996. He was a faculty member at the Department of Statistics and Actuarial Science, University of Waterloo, Canada (2004-2007) and a faculty member at the Department of Mathematics and Statistics, York University, Toronto, Canada (1996-2004). Dr. Song's research interests include bioinformatics, longitudinal data analysis, meta analysis, missing data problems, and statistical genetics. He is interested in methodological developments related to modelling, statistical inference and applications in biomedical sciences. Dr. Song was awarded to prestigious John von Neumann’s Professorship at Technical University of Munich, Germany in 2013. He was an Elected Member of International Statistical Institute. Dr. Song now serves as an Associate Editor of Canadian Journal of Statistics, Statistica Sinica, and Sankhya.
内容提要：
Motivated by the needs of analyzing massive panel imaging data, we present an extension of GeoCopula proposed by Bai, Kang and Song (2014). This new model, termed as imageCopula, helps us to address multilevel spatial-temporal dependencies arising from panel 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 atUniversity of Michigan.
