Regularized rank-based estimation of high-dimensional nonparanormal graphical models Page
1 The Annals of Statistics 2012, Vol. 40, No. 5, 2541–2571 DOI: 10.1214/12-AOS1041 © Institute …

We introduce a constrained empirical loss minimization framework for estimating high-
dimensional sparse precision matrices and propose a new loss function, called the D-trace …

Chandrasekaran, Parrilo, and Willsky proposed a convex optimization problem for graphical
model selection in the presence of unobserved variables. This convex optimization problem …

We propose a novel algorithm for the support estimation of partially known Gaussian
graphical models that incorporates prior information about the underlying graph. In contrast …

The main goal of this paper is to determine the factors responsible for economic growth at
the global level. The indication of the sources of economic growth may be an important …

This study is set out to identify feasible ways for immigrants' integration into the major ten
host countries within the European Union (EU-10) and increased labor market performance …

In this article, we propose a new class of priors for Bayesian inference with multiple
Gaussian graphical models. We introduce Bayesian treatments of two popular procedures …

Standard Gaussian graphical models implicitly assume that the conditional independence
among variables is common to all observations in the sample. However, in practice …

This dissertation focuses on Bayesian inference in nonparanormal graphical models, a
nonparametric extension of Gaussian graphical models. We develop computational …

Given the global importance of bioeconomy for sustainable development and its trendiness
in the knowledge driven literature, our research aims to develop a general assessment …