The 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 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. Moreover, Lasso penalization shrinks the largest eigenvalues of the estimated precision matrix. In this article, we focus on D-trace estimation methodology of a precision matrix. We propose imposing a negative trace penalization on the objective function of the D-trace approach, aimed to control the eigenvalues of the estimated precision matrix. Through extensive numerical analysis, using simulated and real datasets, we show the advantageous performance of our proposed methodology.
|Number of pages||18|
|Journal||Communications in statistics: Simulation and computation|
|Publication status||E-pub ahead of print - 12 Mar 2019|
- Gaussian graphical model
- gene expression
- Hannan-Quinn information criterion
- trace penalization