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Title: Graphical models for stationary time series Authors:  Sumanta Basu - Cornell University (United States) [presenting]
Abstract: Graphical models offer a powerful framework to capture intertemporal and contemporaneous relationships among the components of a stationary multivariate time series. These relationships are encoded in the multivariate spectral density matrix and its inverse. We will present adaptive thresholding and penalization methods for the estimation of these objects under suitable sparsity assumptions. We will discuss new optimization algorithms and investigate the consistency of estimation under a double-asymptotic regime where the dimension of the time series increases with sample size.