報(bào)告題目:  Testing Multiple Hypothesis in Big Data Analysis (大數(shù)據(jù)分析中的多元假設(shè)實(shí)驗(yàn))

報(bào)告人： Dr. Zhigen Zhao

報(bào)告內(nèi)容:

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.

時(shí)間：2015年6月29日上午 10:15--11:15

地點(diǎn)：犀浦校區(qū)2511

主講人簡(jiǎn)介：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.

主辦：研究生院

承辦：kaiyun開(kāi)云官方網(wǎng)站