Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
Zeda Li, Paul H. Chook Department of Information Systems and Statistics, City University of New York, will present, “Adaptive Bayesian Time-Frequency Analysis of Multivariate Time Series”.
Abstract: Understanding cyclical patterns in multiple nonstationary time series, or multivariate time-varying spectral analysis, is important in a variety of fields such as biomedicine, economics, and environmental science. The fundamental unit in multivariate spectral analysis is the power spectrum, a complex matrix valued function of frequency. The complex structure of multivariate time-varying power spectra presents many challenges that have impeded the scope of processes and questions that can be addressed through existing methods. While methods for univariate time series are rather extensive, existing methods for estimating the time-varying spectrum of a multivariate time series are relatively few. This talk has three goals. (1) Discuss the fundamental ideas behind spectral analysis. (2) Discuss the adaptive Bayesian time-frequency analysis of multivariate time series recently introduced by Li and Krafty (2017+) that allows analyzing power spectrum flexibly and efficiently. (3) Explore open research questions in multivariate spectral analysis brought about by complicated modern data structures.
Last Updated On Friday, May 25, 2018 by Kapko, Bernadette E
Created On Friday, May 25, 2018