【10月16日】统计学学术讲座
信息来源：统计与数学学院
作者：统计与数学学院
发布时间：2017-10-16
主题： Network cross-validation by edge sampling
主讲人：朱冀 美国密歇根大学统计系教授
主持人：石磊 统计与数学学院院长
时间：2017 年 10 月 16 日（周一）上午 10:00-11:00
地点：北院卓远楼 305
主办单位：统计与数学学院
摘要：Many models and methods are now available for network analysis, but model selection and tuning remain challenging. Cross-validation is a useful general tool for these tasks in many settings, but is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. Here we propose a new network cross-validation strategy based on splitting edges rather than nodes, which avoids losing information and is applicable to a wide range of network problems. We provide a theoretical justification for our method in a general setting, and in particular show that the method has good asymptotic properties under the stochastic block model. Numerical results on simulated networks show that our approach performs well for a number of model selection and parameter tuning tasks. We also analyze a citation network of statisticians, with meaningful research communities emerging from the analysis. This is joint work with Tianxi Li and Elizaveta Levina.
朱冀简介：美国斯坦福大学统计学博士，美国密歇根大学统计系教授，研究领域为统计机器 学习与数据挖掘、研究兴趣包括高维数据分析、网络数据分析等，在国际主流学术刊物上共 发表 70 多篇学术论文，担任包括国际统计学顶尖刊物《Journal of the American Statistical Association》、《Biometrika》在内的多个期刊副主编。
主讲人：朱冀 美国密歇根大学统计系教授
主持人：石磊 统计与数学学院院长
时间：2017 年 10 月 16 日（周一）上午 10:00-11:00
地点：北院卓远楼 305
主办单位：统计与数学学院
摘要：Many models and methods are now available for network analysis, but model selection and tuning remain challenging. Cross-validation is a useful general tool for these tasks in many settings, but is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. Here we propose a new network cross-validation strategy based on splitting edges rather than nodes, which avoids losing information and is applicable to a wide range of network problems. We provide a theoretical justification for our method in a general setting, and in particular show that the method has good asymptotic properties under the stochastic block model. Numerical results on simulated networks show that our approach performs well for a number of model selection and parameter tuning tasks. We also analyze a citation network of statisticians, with meaningful research communities emerging from the analysis. This is joint work with Tianxi Li and Elizaveta Levina.
朱冀简介：美国斯坦福大学统计学博士，美国密歇根大学统计系教授，研究领域为统计机器 学习与数据挖掘、研究兴趣包括高维数据分析、网络数据分析等，在国际主流学术刊物上共 发表 70 多篇学术论文，担任包括国际统计学顶尖刊物《Journal of the American Statistical Association》、《Biometrika》在内的多个期刊副主编。
