Theory Seminar

High-dimensional covariance estimation based on Gaussian graphical models

Shuheng Zhou

Friday, November 18, 2011
10:00am - 11:00am
East Hall B844

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About the Event

Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using ℓ1-penalization methods. This talk presents the following method. We combine a multiple regression approach with ideas of thresholding and refitting: first we infer a sparse undirected graphical model structure via thresholding of each among many ℓ1-norm penalized regression functions; we then estimate the covariance matrix and its inverse using the maximum likelihood estimator. Under suitable conditions, this approach yields consistent estimation in terms of graphical structure and fast convergence rates with respect to the operator and Frobenius norm for the covariance matrix and its inverse. We also derive an explicit bound for the Kullback Leibler divergence. This is joint work with Philipp Rutimann, Min Xu, and Peter Buhlmann.

Additional Information

Contact: Martin Strauss

Email: martinjs

Sponsor(s): EECS

Open to: Public