报告题目：Linear hypothesis testing for high dimensional generalized linear models
报 告 人：陈钊青年研究员 复旦大学
报告时间：2019年10月21日下午15：00-16：00
报告地点：数学楼第二报告厅
报告摘要：
This paper is concerned with testing linear hypotheses in high dimensional generalized linear models. To deal with linear hypotheses, we first propose the constrained partial regularization method and study its statistical properties. We further introduce an algorithm for solving regularization problems with folded-concave penalty functions and linear constraints. To test linear hypotheses, we propose a partial penalized likelihood ratio test, a partial penalized score test and a partial penalized Wald test. We show that the limiting null distributions of these three test statistics are χ2 distribution with the same degrees of freedom, and under local alternatives, they asymptotically follow noncentral χ2 distributions with the same degrees of freedom and noncentral parameter, provided the number of parameters involved in the test hypothesis grows to ∞ at a certain rate. Simulation studies are conducted to examine the finite sample performance of the proposed tests. Empirical analysis of a real data example is used to illustrate the proposed testing procedures.
报告人简介：
陈钊，青年研究员，2012年在中国科学技术大学获得博士学位。之后在美国普林斯顿大学，宾夕法尼亚州立大学从事博士后研究及研究型助理教授工作。主要研究方向包括高维统计推断，稳健回归，时间序列，非参数及半参数统计方法，以及将统计方法应用于建筑能源，生物信息，癌症研究等领域。科研成果发表在AoS, JASA, JoE, Statistica Sinica, Energy and buildings等期刊上。
