421e446eb2c62410VgnVCM100000c2b1d38dRCRDapproved/UMICH/stats/Home/News & Events/Statistics SeminarDepartment Seminar Series: Sumanta Basu, Estimation in High-dimensional Vector Autoregressive Models###@###(Fri, 22 Nov 2013)Department Seminar Series: Sumanta Basu, Estimation in High-dimensional Vector Autoregressive Models###@###(Fri, 22 Nov 2013)340 West Hall <br>Refreshments will be served at 11:00 AM in the Statistics Lounge, 450 West Hallstats1385137800000138514140000011:30 AM12:30 PM<p>Abstract: Vector Autoregression (VAR) is a widely used method for learning complex interrelationship among the components of multiple time series. Over the years it has gained popularity in the fields of control theory, statistics, economics, finance, genetics and neuroscience. We consider the problem of estimating stable VAR models in a high-dimensional setting, where both the number of time series and the VAR order are allowed to grow with sample size. In addition to the “curse of dimensionality” introduced by a quadratically growing dimension of the parameter space, VAR estimation poses considerable challenges due to the temporal and cross-sectional dependence in the data. Under a sparsity assumption on the model transition matrices, we establish estimation and prediction consistency of $\ell_1$-penalized least squares and likelihood based methods. Exploiting spectral properties of stationary VAR processes, we develop novel theoretical techniques that provide insight into the effect of dependence on the convergence rates of the estimates. We study the impact of error correlations on the estimation problem and develop fast, parallelizable algorithms for penalized likelihood based VAR estimates.</p>Nbzunigabzuniga1384794904007121e446eb2c62410VgnVCM100000c2b1d38d____once11112newnewSumanta Basu, Ph.D. Student, Department of Statistics, University of Michiganhttp://sitemaker.umich.edu/sumbose/home/lsa/LSA Event/Colloquium | Seminar