BIMSA > Data Analysis and Problem Solving Seminar Data Analysis and Problem Solving Seminar Exploratory Functional Data Analysis
Exploratory Functional Data Analysis
Organizer
Xiaoming John Zhang
Speaker
Wenlin Dai
Time
Friday, April 24, 2026 3:00 PM - 4:00 PM
Venue
A3-1-301
Online
Zoom 204 323 0165 (BIMSA)
Abstract
With the advance of technology, functional data are being recorded more frequently, whether over one-dimensional or multi-dimensional domains. Due to the high dimensionality and complex features of functional data, exploratory data analysis faces significant challenges. To meet the demands of practical applications, researchers have developed various tools, including visualization tools, outlier detection techniques, and clustering methods that can handle diverse types of functional data. This talk offers a comprehensive overview of recent procedures for exploratory functional data analysis (EFDA). It begins by introducing fundamental statistical concepts, such as mean and covariance functions, as well as robust statistics such as the median and quantiles in multivariate functional data. Then, we review popular visualization methods for functional data, such as the rainbow plot, and various versions of the functional boxplot, each designed to accommodate different features of functional data. In addition to visualization tools, we also review outlier detection methods, which are commonly integrated with visualization methods to identify anomalous patterns within the data. All the reviewed methods have been implemented in an R package named EFDA.
Speaker Intro
Dr. Dai is an Associate Professor at the Institute of Statistics and Big Data, Renmin University of China. He earned his Ph.D. in Statistics from the Department of Mathematics at Hong Kong Baptist University. His research focuses on nonparametric statistics, complex data analysis, and applied statistics. He has authored over 30 papers published in leading journals, including the Journal of the American Statistical Association, Journal of Machine Learning Research, Statistical Science, and Science China Mathematics. Additionally, he has secured competitive research grants from both the National Natural Science Foundation of China and the National Social Science Foundation of China.