Avagyan, VaheUniversidad Carlos III de Madrid. Departamento de Estadística2016-07-212016-07-212016-072387-0303https://hdl.handle.net/10016/23410The estimation of a precision matrix has an important role in several research fields. In high-dimensional settings, one of the most prominent approaches to estimate the precision matrix is the ɭ₁ (Lasso) norm penalized convex optimization. This framework guarantees the sparsity of the estimated precision matrix. However, it does not control the eigenspectrum of the obtained estimator, and, moreover, it shrinks the largest eigenvalues of the estimated precision matrix. In this paper, we focus on D-trace precision matrix methodology. We propose imposing a negative trace penalization on the objective function of the D-trace approach, aimed to control the eigenvalues. Through extensive numerical analysis, using simulated and real datasets, we show the advantageous performance of our proposed methodology.application/pdfengAtribución-NoComercial-SinDerivadas 3.0 EspañaGaussian Graphical ModelHannan-Quinn Information CriterionD-traceGene expressionTrace penalizationworking paperDT/0000001470