Time Series Analysis

Wavelet-Based Modeling of Dependence Structures in Nonstationary Multivariate Time Series

Haibo Wu, Ph.D. Student, Statistics

Nov 4, 15:00 - 17:00

B3 R5209

Wavelets dynamical modeling Time Series Time Series Analysis multivariate nonstationary extreme data multivariate statistics

This thesis develops a unified wavelet-based statistical framework for modeling time-varying, multiscale dependence structures in nonstationary multivariate time series, to establish a rigorous and versatile statistical foundation for wavelet-based dependence analysis, progressing from pairwise crossscale coherence to canonical and partial canonical coherence between sets of signals.

Ziling Ma

Ph.D. Student, Statistics

Time Series Analysis robust statistics nonparametric statistics

Xiang Chen

Ph.D. Student (former), Statistics

spatio-temporal modeling infectious disease geospatial statistics Spatial epidemiology Public Health Time Series Analysis deep learning explainable AI

Xiang Chen's research focuses on spatio-temporal modeling of climate-sensitive infectious diseases, integrating large-scale health, climate, and human mobility data to improve epidemiological forecasting and support public health decision-making.

Sipan Aslan

Postdoctoral Research Fellow, Statistics

Time Series Analysis Causal Connectivity Dynamic Clustering

Sipan Aslan is a Postdoctoral Research fellow of the Biostatistics group under the supervision of Prof. Hernando Ombao.

Fai Alismail

M.S. Student, Bioengineering

data analysis EEG Signals Modeling Time Series Analysis applied statistics.

Fai Khalid Alismail's research interests extend to the analysis of brain signals, particularly focusing on identifying biomarkers that can distinguish children with ADHD (Attention Deficit Hyperactivity Disorder) from others. This research direction aligns with her academic background in bioinformatics and biostatistics, as well as her professional experience as a Data Scientist. Her goal is to contribute to the field of neuroscience by leveraging advanced statistical methods to analyze EEG (Electroencephalography) data. EEG data provides valuable insights into brain activity and can serve as