George Michailidis, Professor
University of Florida
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.
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.