High dimensional sparse covariance estimation via directed acyclic graphs

Statistics – Methodology

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

10.1214/09-EJS534

We present a graph-based technique for estimating sparse covariance matrices and their inverses from high-dimensional data. The method is based on learning a directed acyclic graph (DAG) and estimating parameters of a multivariate Gaussian distribution based on a DAG. For inferring the underlying DAG we use the PC-algorithm and for estimating the DAG-based covariance matrix and its inverse, we use a Cholesky decomposition approach which provides a positive (semi-)definite sparse estimate. We present a consistency result in the high-dimensional framework and we compare our method with the Glasso for simulated and real data.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

High dimensional sparse covariance estimation via directed acyclic graphs does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with High dimensional sparse covariance estimation via directed acyclic graphs, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and High dimensional sparse covariance estimation via directed acyclic graphs will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-150580

All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.