Topic of Lecture: Bayesian Analysis of General Linear Hypothesis Testing in the High-dimensional Setting
Time of Lecture: July 23, 2018 10:30-11:30 a.m.
Location of Lecture: Mingli Building B306
Lecturer: Wang Min
Description:With the development and popularization of computer technologies, the collection and storage of high-dimensional data (such as financial market data and communication data) has become possible and received extensive attention. Therefore, the analysis of high-dimensional data has brought challenges to traditional analytic theories and methods, while introducing new research directions and opportunities.
About the Lecturer:Wang Min, associate professor at Department of Mathematics & Statistics, Texas Tech University. He obtained a master degree in statistics at Clemson University in May 2010 and a doctor degree in statistics at Clemson University in May 2013. During Aug. 2013 – Dec. 2017, he worked at Department of Mathematical Sciences, Michigan Technological University. He was promoted to associate professor and qualified for tenured professorship in Aug. 2017. He’s been engaged in teaching and research in Texas Tech University since Jan. 2018. In recent years, he participated in and led research projects from NSF and NIH. He has published more than 40 high-level research papers on peer-reviewed authoritative journals.
Fields of Research:Bayesian statistics, computational statistics, statistical inference, analysis and statistical applications of high-dimensional data
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