Structured Dynamic Graphical Models & Scaling Multivariate Time Series

Dates: July 29, 2016
Time: 14:30 – 17:00
Agenda: Click here for a full agenda

VIDEO

 

 

Speaker: Professor Mike West

Title: Structured Dynamic Graphical Models & Scaling Multivariate Time Series

Abstract: Mike West, visiting speaker from Duke University, discusses some of their recent R&D with dynamic statistical models for multivariate time series forecasting that represents a shift in modelling approaches in response to the coupled challenges of scalability and model complexity. Building “simple” and computationally tractable models of univariate time series is a starting point. Decouple/Recouple is an overlaid strategy for coherent Bayesian analysis: That is, “decouple” a high-dimensional system into the lowest-level components for simple/fast analysis; and then, “recouple”– on a sound theoretical basis– to rebuild the larger multivariate process for full/formal/coherent inferences and predictions.

He discusses Bayesian dynamic dependency networks (DDNs) and the broader class of simultaneous graphical dynamic linear models (SGDLMs) that define a framework to address these goals. Aspects of model specification, fitting and computation include importance sampling and variational Bayes methods to implement sequential analysis and forecasting. Studies in financial time series forecasting and portfolio decisions highlight the utility of the models. The advances in Bayesian dynamic modelling– and in thinking about coherent and implementable strategies for scalability to higher-dimensions (i.e. to “big, dynamic data”)– are nicely exemplified in these contexts.

Aspects of this talk represent recent joint work with: Zoey Zhao, 2013 PhD at Duke University, now at Citadel llc, Chicago; Lutz Gruber, 2015 PhD at the Technical University of Munich, now at Quantco, Cologne; and Meng Amy Xie, 2012 BS at Duke University, and current PhD student in Statistical Science at Duke.

Biography:
Mike West holds a Duke University distinguished chair as the Arts & Sciences Professor of Statistics & Decision Sciences in the Department of Statistical Science, where he led the development of statistics from 1990-2002. A past president of the International Society for Bayesian Analysis (ISBA), West has served the international statistics profession in founding roles for ISBA, the National Institute of Statistical Sciences and the Statistical & Applied Mathematical Sciences Institute in the USA, and on advisory boards of national research institutes in UK and Japan, among other professional activities.

West works in Bayesian statistical methods and application, with over 180 papers, 3 books and several edited volumes related to core statistics and interdisciplinary applications in business, econometrics and finance, sig-nal processing, climatology, public health, genomics, immunology, neurophysiology, systems biology and other areas. West has received a number of international awards for research and professional service, including the international Mitchell Prize for research in applied Bayesian statistics (three times), the American Statistical As-sociation JASA award (twice), the Zellner Medal of ISBA (in 2014), and multiple distinguished speaking awards. He has been a statistical consultant for multiple companies, banks, government agencies and academic centers, co-founder of a successful biotech company, and past or current advisor or board member for several financial and IT companies.

West teaches broadly in Bayesian statistics, in academia and through short-courses, works with and advises many undergraduates and Master’s students, and has mentored more than 55 primary PhD students and postdoctoral associates, most of whom are now in academic, industrial or governmental positions involving advanced statistical research.