| Biography | |
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 Prof. Jin Ting Zhang National University of Singapore, Singapore |
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| Title: Two-sample tests for equal distributions in separate metric space:maximum mean discrepancy based approaches | |
| Abstract: Testing the equality of two distributions based on two independent samples generated in some separable metric space is a fundamental hypothesis testing problem in statistics. It is broadly applicable in identifying similarity or distinction of two complicated data sets (e.g., high-dimensional data or functional data) collected in a wide range of research or industry areas, including biology, bioinformatics, medicine, material science, among others. Recently a so-called maximum mean discrepancy (MMD) based approach for the above two-sample problem has been proposed, resulting in several interesting tests. However, the main theoretical and numerical results of these MMD based tests depend on a very restricted assumption that the two samples have the same sample sizes. In real data analysis, this equal-sample-size assumption is hardly satisfied and discarding some of the observations often means a loss of priceless information. In this talk, we give a brief review of the MMD based tests and propose a new MMD based test with the equal-sample-size assumption removed. The asymptotic null and alternative distributions of the MMD based test statistic and its root-n consistency are established. Methods for approximating the null distribution, resulting in easy and quick implementation, are proposed. Numerical experiments based on artificial data and six real data sets from different areas of applications demonstrate that the resulting test works very well in terms of size control and power, when it is compared with several existing competitors. (This is a joint work with Dr. Smaga, Adam Mickiewicz University, Poland.) | |
| Biography: Professor Jin-Ting Zhang was born in Guangdong, China. He got a Bachelor degree in Peking University, China, a Master degree in Academia Sinica, Beijing, China, and a PhD degree in North Carolina University at Chapel Hill, USA. He was a postdoc research fellow at Harvard University. He was a visiting research fellow at University of Rochester and Princeton University, USA. He is a tenured Full Professor at Department of Statistics and Applied Probability, National University of Singapore. He has trained seven PhD students and a number of postdoc fellows. He has published more than 70 research papers, two monographs, and an edited book of research papers. He has been Associate Editor of a few statistical journals. He served as a member of several scientific committees for several big international conferences. Research areas of Professor Zhang include Nonparametric Regression, Longitudinal data analysis, Functional data analysis and High-dimensional data analysis among others. | |
