EE Student Information

The Department of Electrical Engineering supports Black Lives Matter. Read more.

• • • • •

EE Student Information, Spring Quarter through Academic Year 2020-2021: FAQs and Updated EE Course List.

Updates will be posted on this page, as well as emailed to the EE student mail list.

Please see Stanford University Health Alerts for course and travel updates.

As always, use your best judgement and consider your own and others' well-being at all times.

Statistics Department Seminar presents "Hypothesis testing for large-scale data: Enhancing reliability and efficiency"

Topic: 
Hypothesis testing for large-scale data: Enhancing reliability and efficiency
Tuesday, January 26, 2021 - 4:30pm
Speaker: 
Yinqiu He (Univ of Michigan)
Abstract / Description: 

In scientific research that involves large-scale data, researchers often start with questions regarding the global properties of a large set of measurements. For instance, are a group of related genes in the same functional pathway jointly associated with a trait of interest? Such questions can be formulated as hypothesis testing problems that globally examine a large number of parameters in a high-dimensional joint distribution. Examples include hypothesis testing on mean vectors, covariance matrices and regression coefficients. To extract informative scientific knowledge from abundant data, reliability and efficiency are among the major concerns in statistical inference.

In this talk, I will address particular reliability and efficiency issues arising from jointly testing a large number of parameters. First, I will discuss how reliable the popular likelihood ratio tests (LRTs) are in terms of the type I error control for high-dimensional data. I will provide theoretical insights into the reliability of the LRTs in a variety of problems, which are based on phase transition results of the foundational Wilk's theorem. Next, to improve efficiency of the existing testing procedures under high-dimensional settings, I will introduce a new adaptive testing framework that can maintain high statistical power against a wide range of alternative hypotheses. The proposed framework is based on a family of U-statistics that are constructed to capture the information in different directions in high-dimensional spaces. For a broad class of problems, we establish high-dimensional asymptotic theory for the U-statistics and develop adaptive testing procedures that are statistically powerful in a wide variety of scenarios.

 


 

The Statistics Seminars for Winter Quarter will be held online via Zoom at 4:30pm on Tuesdays. Please note that these events will be locked by the Host after ten minutes, so latecomers will not be able to join the Meeting. Subscribe to Statistics distribution list to receive Meeting IDs and passwords via email.