Topic: Testing Multiple Hypothesis in
Big Data Analysis
Speaker： Dr. Zhigen Zhao
Abstract:
When testing one single hypothesis, it is given in every introduction statistics
textbook that one should reject the null hypothesis if the p-value is less than
or equal to alpha, a designated level reflecting one's willingness of
tolerating the type I error. In modern application, it is a common practice for
a scientist to test thousands, or even millions of hypothesis simultaneously.
Can we still use the simple rule stated earlier?
In this talk, I will present
the issue of the multiplicity when testing multiple hypothesis and the recent
development on this area in the last two decades. I will also discuss my recent
work on the construction of the optimal multiple testing procedures using the
decision empirical Bayes approach. I will highlight a few applications with big
data, arising from studies such as the genetics and social science.
Time：10:15--11:15, June 29, 2015
Room：X2511
Profile：Dr. Zhigen Zhao is an assistant professor at Department of Statistics, Temple University
(USA). He got his Ph.D. in mathematics from Cornell University in 2009.
