This article reviews the literature on sparse high-dimensional models and discusses some
applications in economics and finance. Recent developments in theory, methods, and …

Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical
analyses from a Bayesian perspective. Using examples largely from the authors' own …

The paper proposes a method for constructing a sparse estimator for the inverse covariance
(concentration) matrix in high-dimensional settings. The estimator uses a penalized normal …

Focusing on Bayesian approaches and computations using simulation-based methods for
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …

High dimensionality comparable to sample size is common in many statistical problems. We
examine covariance matrix estimation in the asymptotic framework that the dimensionality p …

This paper studies the sparsistency and rates of convergence for estimating sparse
covariance and precision matrices based on penalized likelihood with nonconvex penalty …

Motivation: Genetic networks are often described statistically using graphical models (eg
Bayesian networks). However, inferring the network structure offers a serious challenge in …

Drawing from the authors' own work and from the most recent developments in the field,
Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity …

Graphical models are frequently used to explore networks, such as genetic networks, among
a set of variables. This is usually carried out via exploring the sparsity of the precision matrix …

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 …