题目：Sparse Generalized Linear Models

报告人：贾金柱（北京大学565net必赢客户端概率统计系）

摘要：I will talk on sparse regressions. Lasso and compressed sensing will be discussed. Especially, I will emphasize on our recently proposed new penalized method to solve sparse Poisson Regression problems. Being different from l1 penalized log-likelihood estimation, our new method can be viewed as a penalized weighted score function method. We show that under mild conditions, our estimator is l1 consistent and the tuning parameter can be pre-specified, which owns the same good property of the square-root Lasso. The simulations show that our proposed method is much more robust than traditional sparse Poisson models using l1 penalized log-likelihood method.

时间：12月21日（周四）上午10:30

地点：首师大校本部新教二楼613

欢迎全体师生积极参加！