| Biography | |
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 Prof. Jin Wang Northern Arizona University, USA |
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| Title: Descriptive Measures for Elliptically Symmetric Distributions and Their Depth-Based Minimum Volume Ellipsoid Estimators | |
| Abstract: Multivariate descriptive measures for location, scale, correlation, skewness, and kurtosis are the foundation of multivariate statistics. Based on the geometric features, we propose some nonparametric descriptive measures for those concepts for elliptically symmetric distributions and their depth-based minimum volume ellipsoid estimators. Those new measures are robust and moment-free, that is, do not require the existence of any moment. The estimators of those descriptive measures possess some favorite properties. Under some regularity conditions, all the estimators are consistent. In addition, both the estimators of the location and scale measures are unbiased, and they are independent. | |
| 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 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) defined a generalized multivariate kurtosis ordering. Based on the ordering, they developed a two-dimensional visual device to compare two distributions in any dimension with respect to spread and kurtosis. In 2019, Wang studied the asymptotic behavior of generalized depth-based spread processes. Based the results, he designed a graphical method to compare spread and kurtosis of two multivariate data sets and a new graphical method to assess multivariate normality. Besides the theoretical researches, Wang is also interested in applications of statistics in various fields to solve practical problems. He worked in industry for eight years (1991-1999) and currently participated in several health-related projects at NAU. | |
