Abstract
In the first part of this lecture we present a review dynamic linear models for multivariate time series and hierarchical dynamic linear models for multiple time series. Topics related to model building as well as closed form, approximate and simulation-based methods for Bayesian filtering, smoothing and forecasting within these classes of models will be discussed. We illustrate the use of these models in case studies involving the analysis of multi-trial and multi-channel electroencephalogram recordings. In the second part of the lecture we present two approaches for frequency-domain time series analysis: an approach for spectral analysis of multiple time series and a flexible and computationally efficient approach for spectral analysis of multivariate time series.
Brief Biography
Raquel Prado is Professor in the Department of Statistics of the Baskin School of Engineering at the University of California Santa Cruz, where she has been on the faculty since 2001. In 1998 she graduated with a PhD in Statistics and Decision Sciences from Duke University. From 1998 until 2001 she was an Assistant Professor in Statistics at Universidad Simon Bolivar in Venezuela. She was a co-recipient of the Outstanding Statistical Application Award of the American Statistical Association in 1999. She is a Fellow of the American Statistical Association. She is also a Fellow and Past President of the International Society for Bayesian Analysis and currently a member of the Committee on Applied and Theoretical Statistics (CATS) of the National Academies of Science, Engineering and Medicine.