主 题：Modeling time-varying effects for high-dimensional covariates: a new Gateaux-differential boosting approach
主讲人：Yi Li 教授
主持人：林华珍教授
时 间：2014年7月28日下午4:00-5:00
地 点：通博楼B座212学术会议室
主办单位：统计学院 统计研究中心 科研处
主讲人简介：
Yi Li 密歇根大学教授，Kidney Epidemiology and Cost Center主任，NSF Statistics and Probability Program的评审专家，是包括统计学顶级期刊Journal of the American Statistical Association、Biometrics、Scandinavian Journal of Statistics在内的五个期刊副主编。 李教授从事统计以及生物统计研究，主持美国国家癌症研究院等基金10多项，基金总计超过2400多万美元，发表论文100余篇，其中在国际顶级杂志Journal of the American Statistical Association、Biometrics、Biometrika、Journal of the Royal Statistical Society:Series B等期刊发表论文20多篇。 为国际生物统计学会，美国统计学会（ASA Fellow，约千分之三的美国统计学会会员获此荣誉），国际数理统计学会以及泛华统计协会会员。
内容提要：
Survival models with time-varying effects provide a flexible framework for modeling the effects of covariates on event times. However, the difficulty of model construction increases dramatically as the number of variables and sample size grow. Existing constrained optimization and boosting methods suffer from computational complexity. We propose a new Gateaux differential-based boosting procedure for simultaneously selecting and automatically determining the functional form of covariates. The proposed method is flexible in that it extends the gradient boosting to functional differentials in general parameter space. At each boosting learning step, only the best-fitting base-learner (and therefore the most informative covariate) is added to the predictor, which consequently encourages sparsity. The performance of the proposed method is examined by simulations and by application to analyze the national kidney transplant data.
