About Haibo Wu Haibo Wu Ph.D. Student, Statistics Haibo Wu is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST), advised by Prof. Hernando Ombao, where his research focuses on developing wavelet-based statistical methodologies for modeling dynamic dependence structures in nonstationary multivariate time series, with applications to neuroscience, finance, and other complex data-rich systems. Events Presented Events Nov 1 - Nov 7, 2026 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.
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.
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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
