统计学系系列讲座之359期

时　间：2019年11月14日（星期四）16:00-17:00

地　点：史带楼602室

主持人：张新生  教授 复旦大学管理学院统计学系

主　题：An improved Bayesian information criterion for high dimensional analysis

主讲人：Professor Wang Zhou

　　　　Department of Statistics and Applied Probability, National University of Singapore

简　介：

Wang Zhou 教授2004年在香港科技大学取得博士学位，2009年至2013年任新加坡国立大学终身副教授，2014年起任终身正教授（Full Professor）。他的主要研究方向为：高维数据的统计推断、网络数据的统计分析等。已在国际统计学领域的顶级期刊如AOS、JASA、Biometrika、AOP、及AOAP上发表论文60余篇。

摘　要：

Information criterion is very important in model selection and variable selection, more so in high dimensional settings. There are several existing ones designed in high dimension settings like high dimensional Bayesian Information Criteria (HBIC) and extended Bayesian Information Criterion (EBIC), which are proved to be useful in both theory and application. However, the subtle balance between unknown parameters and the complexity of the model is worthy to be further studied. In the paper, we propose a new Bayesian information criterion, which allows the dimensionality of covariates to grow exponentially fast with the sample size. Model selection consistency for both unpenalized and penalized estimators are established. Extensive simulation studies in commonly used models show that our information criterion has substantial improvement against other major competitors. Thus we name our method as improved Bayesian Information Criterion (IBIC). Moreover, we extend IBIC to select thresholding parameter for sparse covariance matrix estimation and the results are promising.

 统计学系 

2019-10-31