Title：Weak Signal Identification and Inference in Penalized Model Selection
Speaker:Prof.Annie Qu, University of Illinois at Urbana-Champaign
Date:2015.5.27(Wednesday)2:30-3:30 PM
Place:苏州大学本部维格堂113
Abstract: Weak signal identification and inference are very important in the area of penalized model selection, yet they are under-developed and not well-studied. Existing inference procedures for penalized estimators are mainly focused on strong signals. In this paper, we propose an identification procedure for weak signals in finite samples, and provide a transition phase in-between noise and strong signal strengths. We also introduce a new two-step inferential method to construct better confidence intervals for the identified weak signals. Both theory and numerical studies indicate that the proposed method leads to better confidence coverage for weak signals, compared with those using asymptotic inference. In addition, the proposed method outperforms the perturbation and bootstrap resampling approaches. We illustrate our method for HIV antiretroviral drug susceptibility data to identify genetic mutations associated with HIV drug resistance. This is joint work with Peibei Shi.
报告人简介:瞿培勇,美国伊利诺伊大学厄巴纳-香槟分校统计系教授，研究生主任，主要研究：高维数据，中文自然语言处理，个性化医学，纵向数据分析，模型选择，非参数模型，混合效应模型，生物统计等。
详见个人网页：//publish.illinois.edu/anniequ/
Weak Signal Identification and Inference in Penalized Model Selection