报告题目1: Forward variable selection for sparse ultra-high dimensional varying coefficient models

报告人：郑明燕（Ming-Yen Cheng），台湾大学讲座教授，美国统计协会会士，数理统计学院会士

报告时间：2015年9月17日(周四)下午1:00

报告地点：数学楼二楼学术报告厅

Abstract: Varying coefficient models have numerous applications in a wide scope of scientific areas. While enjoying nice interpretability, they also allow for flexibility in modeling dynamic impacts of the covariates. But, in the new era of big data, it is challenging to select the relevant variables when the dimensionality is very large. Recently several works are focused on this important problem based on sparsity assumptions; they are subject to some limitations, however. We introduce an appealing forward selection procedure. It selects important variables sequentially according to a reduction in sum of squares criterion and it employs a BIC-based stopping rule. Clearly it is simple to implement and fast to compute, and possesses many other desirable properties from theoretical and numerical viewpoints. The BIC is a special case of the EBIC when an extra tuning parameter in the latter vanishes. We establish rigorous screening consistency results when either BIC or EBIC is used as the stopping criterion. The theoretical results depend on some conditions on the eigenvalues related to the design matrices, which can be relaxed in some situations. Results of an extensive simulation study and a real data example are also presented to show the efficacy and usefulness of our procedure.

报告人简介：
郑明燕（Ming-Yen Cheng），台湾大学数学系讲座教授，1994年博士毕业于美国北卡罗来纳大学教堂山分校，曾任英国伦敦大学学院（University College London）统计科学系（Department of Statistical Science）系主任，历任统计学顶级期刊Annals of Statistics, Journal of the American Statistical Association编委，荣获台湾数学学会青年数学家奖。郑教授在统计学核心SCI期刊发表论文30篇，包括在顶级期刊Annals of Statistics, Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society B 发表论文13篇。