统计与管理学院2017年学术报告第75期

【主 题】 Linear hypothesis testing for high dimensional generalized linear models

【报告人】 Runze Li, 教授

Pennsylvania State University

【时 间】 2017年12月19日（星期二）09:00-10:00

【地 点】 上海财经大学统计与管理学院大楼1208会议室

【摘 要】This paper is concerned with testing linear hypotheses in high-dimensional generalized linear models. To deal with linear hypotheses, we first propose 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 further show that the limiting null distributions of these three test statistics are chi-square distribution with the same degrees of freedom, and under local alternatives, they asymptotically follow non-central chi-square distributions with the same degrees of freedom and noncentral parameter. 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.

【嘉宾简介】http://stat.psu.edu/people/ril4