This review covers recent results concerning the estimation of large covariance matrices
using tools from Random Matrix Theory (RMT). We introduce several RMT methods and …

The estimation of large covariance and precision matrices is fundamental in modern
multivariate analysis. However, problems arise from the statistical analysis of large panel …

The paper deals with the estimation of a high dimensional covariance with a conditional
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

Many econometric and data-science applications require a reliable estimate of the
covariance matrix, such as Markowitz's portfolio selection. When the number of variables is …

We develop a penalized likelihood estimation framework to learn the structure of Gaussian
Bayesian networks from observational data. In contrast to recent methods which accelerate …

Networks effectively capture interactions among components of complex systems, and have
thus become a mainstay in many scientific disciplines. Growing evidence, especially from …

We clarify relationships between conditional (CAR) and simultaneous (SAR) autoregressive
models. We review the literature on this topic and find that it is mostly incomplete. Our main …

We consider a Bayesian framework for estimating a high-dimensional sparse precision
matrix, in which adaptive shrinkage and sparsity are induced by a mixture of Laplace priors …

Previously in “Reconciling solar forecasts: Geographical hierarchy”[Solar Energy 146 (2017)
276–286], we have demonstrated that by reconciling the forecasts obtained from a …