A Review of Bayesian Variable Selection Methods: What, How and Which Page 1 Bayesian
Analysis (2009) 4, Number 1, pp. 85–118 A Review of Bayesian Variable Selection Methods …

Due to the advancements in sensing technologies and computational capabilities, system
health assessment and prognostics have been extensively investigated in the literature …

Supplement to “Penalising Model Component Complexity: A Principled, Practical Approach
to Constructing Priors”. The supplementary material contains the proofs of all theorems …

Monetary policy and the private sector behaviour of the US economy are modelled as a time
varying structural vector autoregression, where the sources of time variation are both the …

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 …

The evolution of Bayesian approaches for model uncertainty over the past decade has been
remarkable. Catalyzed by advances in methods and technology for posterior computation …

We propose a nonparametric method for identifying parsimony and for producing a
statistically efficient estimator of a large covariance matrix. We reparameterise a covariance …