Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 3 (2009), 1133-1160.
High dimensional sparse covariance estimation via directed acyclic graphs
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 [12, 8, 2] for simulated and real data.
Electron. J. Statist., Volume 3 (2009), 1133-1160.
First available in Project Euclid: 1 December 2009
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Rütimann, Philipp; Bühlmann, Peter. High dimensional sparse covariance estimation via directed acyclic graphs. Electron. J. Statist. 3 (2009), 1133--1160. doi:10.1214/09-EJS534. https://projecteuclid.org/euclid.ejs/1259677088