Covariance estimation for high-dimensional datasets is a fundamental problem in machine
learning, and has numerous applications. In these high-dimensional settings the number of …

Covariance estimation for high-dimensional datasets is a fundamental problem in modern
day statistics with numerous applications. In these high dimensional datasets, the number of …

Learning covariance matrices from high-dimensional data is an important problem that has
received a lot of attention recently. We are particularly interested in the high-dimensional …

Estimation of a large sparse covariance matrix is of great importance for statistical analysis,
especially in high‐dimensional settings. The traditional approach such as the sample …

The paper proposes a new covariance estimator for large covariance matrices when the
variables have a natural ordering. Using the Cholesky decomposition of the inverse, we …

Pairwise likelihood is a useful approximation to the full likelihood function for covariance
estimation in high-dimensional context. It simplifies high-dimensional dependencies by …

Methods for estimating sparse and large covariance matrices Covariance and correlation
matrices play fundamental roles in every aspect of the analysis of multivariate data collected …

The sparse inverse covariance estimation problem is commonly solved using an $\ell_ {1} $-
regularized Gaussian maximum likelihood estimator known as “graphical lasso”, but its …

Estimation of sparse covariance matrices and their inverse subject to positive definiteness
constraints has drawn a lot of attention in recent years. The abundance of high-dimensional …

Fitting high-dimensional data involves a delicate tradeoff between faithful representation
and the use of sparse models. Too often, sparsity assumptions on the fitted model are too …