Prof.Yuhong Yang 杨宇红

University of Minnesota

杨宇红教授1996年获得耶鲁大学统计学博士，现为明尼苏达大学统计系教授及Director of Graduate Studies。他曾荣获美国国家科学基金会杰出青年教授奖（NSF CAREER Award），此奖项每年只有1-2名学者获此殊荣。并于2010年成为（国际）数理统计学会会士。曾主持美国自然科学基金项目4项。其研究兴趣包括高维数据分析理论、模型选择和组合、多臂老虎机问题（Multi-Arm Bandit）、精准医学统计、预测，并在这些领域建立了很多重要且深刻的理论和方法，发表论文70余篇，其中18篇为独立作者（single author）。这些论文发表在统计、机器学习、信息论、计量经济、预测、逼近论等领域顶尖刊物，如Annals of Statistics, JASA, Biometrika, JRSSB, IEEE Transactions on Information Theory, Journal of Econometrics等，在Google Scholar上的引用多达4000多次。

Abstract

Model selection and its diagnosis are foundational elements in modern statistical and machine learning applications that serve the purpose of obtaining reliable information and reproducible results. In this short course, we introduce the principles and theories on model selection and model averaging and their applications in high-dimensional regression. Model selection methods include information criteria (AIC, BIC etc), cross validation, penalized regression (LASSO, SCAD, MCP) and more. We will learn to understand their differences, connections, performances, limitations, proper uses, and approaches to achieving the best performance without knowing which method is the best for the data at hand. In addition, we will study new tools to characterize model selection reliability. When model selection uncertainty is high, model averaging/combining typically offers more accurate prediction and more reliable conclusions. Theoretical results covered include model selection consistency, consistent cross validation, adaptive minimax optimal regression learning in high-dimensional regression, and optimalities of model averaging methods.

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