Title：Graph Based Nonparametric Testing for High Dimensional Data

　　Time：2019年1月9日周三下午3：00

　　Venue：软件所5号楼708会议室

　　Abstract：

　　This talk will focus on a common applied High-dimensional k-sample comparison problem. We constructed a class of easy-to-implement nonparametric distribution-free tests based on new statistical tools and unexplored connections with spectral graph theory. The test is shown to possess various desirable properties along with a characteristic exploratory flavor that has practical consequences. The numerical examples show that our method works surprisingly well under a broad range of situations.

　    Bio:：

　　Kaijun Wang is a PhD Candidate at the Department of Statistical Science, Temple University, working with Prof. Subhadeep Mukhopadyay.