High dimensional sparse covariance estimation via directed acyclic graphs
Methodology
2010-01-18 v2
Abstract
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.
Cite
@article{arxiv.0911.2375,
title = {High dimensional sparse covariance estimation via directed acyclic graphs},
author = {Philipp Rütimann and Peter Bühlmann},
journal= {arXiv preprint arXiv:0911.2375},
year = {2010}
}