报告人：李亚光（中国科学技术大学）

时间：2021年4月3日10：50开始

地址：数统学院LD202

摘要:We consider the problem of both prediction and model selection in high dimensional generalized linear models. Predictive performance can be improved by leveraging structure information among predictors. In this paper, a graphic model-based doubly sparse regularized estimator is discussed under the high dimensional generalized linear models, that utilizes the graph structure among the predictors. The graphic information among predictors is incorporated node-by-node using a decomposed representation and the sparsity is encouraged both within and between the decomposed components. We propose an efficient iterative proximal algorithm to solve the optimization problem. Statistical convergence rates and selection consistency for the doubly sparse regularized estimator are established in the ultra-high dimensional setting. Specifically, we allow the dimensionality grows exponentially with the sample size. We compare the estimator with existing methods through numerical analysis on both simulation study and a microbiome data analysis.

简介:李亚光，中国科学技术大学管理学院博士后 ，2018年毕业于中国科学技术大学，获得统计学博士学位，后在多伦多大学Dalla Lana公共卫生学院从事博士后研究，先后访问过新加坡国立大学和约克大学。目前为中国科学技术大学管理学院博士后。主要从事高维数据分析，变点检测和个性化医疗等领域的研究。在Electronic Journal of Statistics, SCIENCE CHINA-Mathematics和Statistics in Medicine等国际知名学术期刊上发表多篇论文。

邀请人：夏小超

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