Seminars

Other UCLA departments frequently hold seminars related to Statistics and of likely of interest to our members. Here are links to UCLA Biostatistics seminars and UCLA Biomath seminars:
https://www.biostat.ucla.edu/2019-seminars
http://www.biomath.ucla.edu/seminars/

How to Subscribe to the UCLA Statistics Seminars Mailing List
Join the UCLA Statistics seminars mailing list by sending an email to sympa@sympa.cts.ucla.edu with “subscribe stat_seminars” (without quotation marks) in the subject field and the message body blank. This needs to be done from the address that is to be subscribed. After doing that please respond to the email that you receive. An automated email will be sent which confirms that you have been added.

How to Unsubscribe from the UCLA Statistics Seminars Mailing List
You may be receiving our seminar emails because you are subscribed to our seminars mailing list (or one of our other mailing lists). You can determine which is the case by looking at the subject line of a seminar email. You may unsubscribe from the seminar mailing list by sending an email to sympa@sympa.cts.ucla.edu with “unsubscribe stat_seminars” (without quotation marks) in the subject field and the message body blank. This needs to be done from the address that is subscribed. After sending that email please follow the directions in the email response that you receive. If you are getting our seminar emails because of a subscription to one of our other mailing lists then the word “seminars” in the subject field must have the appropriate replacement.

Tuesday, 01/28/2020, Time: 11:00am – 12:00pm
Some Recent Advances in Modeling, Estimation and Inference for Vector Autoregressive Models

Posted: January 24, 2020 | 0 comments

Rolfe 3126

George Michailidis, Professor
University of Florida

Abstract:
Vector autoregressive models capture temporal interconnections between temporally evolving entities (variables). They have been extensively used in macroeconomic and financial modeling and more recently they have found novel applications in functional genomics and neuroscience. In this presentation, I provide a brief overview of recent advances on their modeling and estimation issues in the high dimensional setting. Subsequently, I discuss some recent results on statistical inference for the model parameters and briefly touch upon issues of robustness. The results are illustrated on both synthetic and real data.

Bio:
George Michailidis is a Professor of Statistics and Director of the Informatics Institute at the University of Florida. His research interests include Multivariate Analysis and Machine Learning, Computational Statistics, Change-point Estimation, Stochastic Processing Networks, Bioinformatics, Network Tomography, Visual Analytics, Statistical Methodology with Applications to Computer, Communications and Sensor Networks.