Dr. Jin Wang
Northern Arizona University, USA
Biography:
Jin Wang received his Ph.D. in statistics from the University of Texas at Dallas and joined Northern Arizona University (NAU) in 2003. His research on statistics started from the multivariate change-point problems at Wuhan University in 1990. The paper was invited to present at the International Symposium on Multivariate Analysis and Its Applications, Hong Kong, in 1992, and published in the IMS Lecture Notes – Monograph Series. His recent research focused on nonparametric multivariate analysis and its applications. The following are some representative works. Wang and Serfling (2005) introduced a nonparametric multivariate kurtosis measure. The measure is not only robust but also discriminates better among distribution shapes. It determines elliptically symmetric distributions up to affine equivalence. In 2009, he proposed a family of kurtosis orderings for multivariate distributions, which is a pioneering work on multivariate kurtosis ordering. Various applications of the orderings have appeared in the literature. Furthermore Wang and Zhou (2012) introduced a generalized multivariate kurtosis ordering. Based on the ordering, they developed a two-dimensional visual device to compare two distributions or two data sets in any dimension with respect to spread and kurtosis. Besides the theoretical studies, Dr. Wang is also interested in applications of statistics in various fields to solve practical problems. He participated in several health-related projects at NAU and some important projects in industry such as “Probabilistic Risk Assessment (PRA) of Daya Bay Nuclear Power Plant”.