科学研究
报告题目:

Robust and Joint Feature Screening in High Dimension

报告人:

夏小超(重庆大学)

报告时间:

报告地点:

老外楼三楼概率与统计科学系教研室

报告摘要:

In this talk, I will give an introduction to ultrahigh-dimensional robust and joint feature screening. Specifically, I first review the seminal work of Fan and Lv (2008). Then, I will present three concrete robust and joint feature screening approaches including: (a) the conditional quantile correlation-based sure independence screening (CQC-SIS) under varying coefficient models, (b) the copula partial correlation-based sure independence screening (CPC-SIS), and (c) a robust partial correlation-based sure independence (RPC-SIS), according to our recent work. For each approach, I will talk about the motivation, methodology, and main theoretical results. Some empirical results would be shown if time permits.

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