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GilbertSaporta教授讲座通知

题目：ASURVEYOFSOME “SPARSE” METHODSFORHIGHDIMENSIONALDATA

主讲人：Prof.GilbertSaporta

时间：2018年9月25日（周二），14:30-16:00pm

地点：新主楼B221

邀请人：王惠文 教授

摘要：

High dimensional data means that the number of variablespis far larger than the number of observationsn. This occurs in several fields such as genomic data or chemometrics.

Whenp>nthe OLS estimator does not exist for linear regression. Since it is a case of forced multicollinearity, one may use regularized techniques such as ridge regression, principal component regression or PLS regression: these methods provide rather robust estimates through a dimension reduction approach or with explicit (or not) constraints on the regression coefficients. The fact that all the predictors are kept is often considered as a positive point.

However, ifp>>nit becomes a drawback since a combination of all variables cannot be interpreted. Sparse combinations,i.e.with a large number of zero coefficients are preferred. The Lasso consists in finding the estimate, which performs simultaneously regularization and variable selection thanks to a L₁penalty. We will present variants such as sparse PLS and the group-lasso when the variables are structured in blocks.

主讲人简介：

GilbertSaporta教授，法国巴黎第六大学博士学位，现任法国统计学会荣誉主席，法国国立工艺学院（CNAM）荣誉教授。曾担任国际统计学会副主席和国际统计计算学会主席。GilbertSaporta教授长期从事数据分析相关基础科学研究，指导了27名博士，发表专著2部，高水平学术论文80余篇。