| 报告题目： | Functional Data Analysis of Imaging Data |
| 报 告 人： | Hongtu Zhu（朱宏图）教授 |
| 美国北卡罗来纳大学教堂山分校（University of North Carolina at Chapel Hill）生物统计系教授 | |
| 报告时间： | 2014.5.20（周二）,14:30-16:00 |
| 报告地点： | 九龙湖数学系第一报告厅 |
| 相关介绍： | 摘要：Motivated by recent work on studying massive imaging data in various neuorimaging studies,our group proposes several classes of spatial regression models including spatially varying coefficient models, spatial predictive Gaussian process models, tensor regression models, and Cox functional linear regression models for the joint analysis of large neuorimaging data and clinical and behavioral data. Our statistical models explicitly account for several stylized features of neuorimaging data: the presence of multiple piecewise smooth regions with unknown edges and jumps and substantial spatial correlations. We develop some fast estimation procedures to simultaneously estimate the varying coefficient functions and the spatial correlations. We systematically investigate the asymptotic properties (e.g., consistency and asymptotic normality) of the multiscale adaptive parameter estimates. Our Monte Carlo simulation and real data analysis have confirmed the excellent performance of our models in different applications. Hongtu Zhu（朱宏图）为美国北卡罗来纳大学教堂山分校（University of North Carolina at Chapel Hill）生物统计系教授，美国统计学会图像统计方向八个创始人之一。现担任国际统计界顶级学术期刊统计年刊Annals of Statistics和美国统计协会杂志Journal of the American Statistical Association副主编，同时还担任Statistica Sinica和Statistics and its Inference副主编。先后被选为美国统计学会（American Statistical Association）终身会员；国际数理统计学会（Institute of Mathematical Statistics，IMS）终身会士。 朱宏图教授多年来一直致力于医学图像统计分析、结构方程模型、统计诊断、变量选择、删失数据分析、函数型数据分析、流形数据统计分析等几个方面的研究，在统计学和生物统计学多个领域作出了贡献。 |
