Network cross-validation by edge sampling
报 告 人:: 朱冀
报告地点:: 数学与统计学院4楼报告厅
报告时间:: 2017年12月04日星期一15:30-16:30
报告简介:

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.

举办单位:数学与统计学院
发 布 人:科研助理 发布时间: 2017-12-04
主讲人简介:
朱冀,毕业于斯坦福大学,现为密歇根大学统计系教授,长期从事统计学,计算机科学,金融工程以及相关交叉学科的研究,其研究领域涉及统计机器学习,数据挖掘,高维数据,网络模型,金融,管理等各个方面,已在国际著名统计刊物上发表学术论文七十余篇。朱冀教授目前担任七家国际著名统计期刊的副主编,以及全美统计协会统计学习和数据挖掘分会会长。2008年获得美国国家科学基金的CAREER奖。