About Zhuo Qu Zhuo Qu Ph.D. Student, Statistics statistics Functional data robust statistics nonparametric statistics spatio-temporal statistics Zhuo Qu is a PhD candidate in statistics, under Prof. Marc Genton. Zhuo likes to use statistics methods to interpret the data in the applications and find the new possibility of exploring the new statistics model. Being eager to learn and experience the uncertainty in the research and life, she also like chatting, swimming and piano. Her goal is to be a spare-time pianist among professional statisticians. Education and Early Career King Abdullah University of Science and Technology, Thuwal, January 2019 - December 2022, PhD in Statistics King Abdullah University of Science and Technology Events Presented Events Nov 6 - Nov 12, 2022 Multivariate Functional Data Analysis and Visualization Zhuo Qu, Ph.D. Student, Statistics Nov 7, 11:00 - 13:00 B3 L5 R5220 functional data analysis multivariate functional data statistics As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.
Multivariate Functional Data Analysis and Visualization Zhuo Qu, Ph.D. Student, Statistics Nov 7, 11:00 - 13:00 B3 L5 R5220 functional data analysis multivariate functional data statistics As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.
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