The INI has a new website!

This is a legacy webpage. Please visit the new site to ensure you are seeing up to date information.

Skip to content



Regularised estimation of high dimensional covariance matrices

Bickel, P (Berkeley)
Tuesday 08 January 2008, 16:30-17:30

Seminar Room 1, Newton Institute


Abstract: We review ,with examples, various important parameters depending on the population covariance matrix such as inverses and eigenstructures , and the uses they are put to.We give a brief discussion of well known pathologies of the empirical covariance matrix in various applications when the data is high dimensional which imply inconsistency of "plug-in"estimates of the parameters mentioned. We introduce different notions of sparsity of such matrices and show how some of these are intimately related. We then review a number of methods taking advantage of such sparsity in the population matrices .In particular we state results with various collaborators, particularly E. Levina establishing rates of convergence of our estimates of parameters as above ,as dimension and sample size tend to oo, that are uniform over large classes of sparse population covariance matrices . We conclude with some simulations , a data analysis supporting the asymptotics, and a discussion of future directions.

Related Links


[pdf ]




The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.

Back to top ∧