RT Journal Article
T1 Improving the Graphical Lasso Estimation for the Precision Matrix Through Roots of the Sample Covariance Matrix
A1 Avagyan, Vahe
A1 Alonso Fernández, Andrés Modesto
A1 Nogales Martin, Francisco J.
AB In this article, we focus on the estimation of a high-dimensional inverse covariance (i.e., precision) matrix. We propose a simple improvement of the graphical Lasso (glasso) framework that is able to attain better statistical performance without increasing significantly the computational cost. The proposed improvement is based on computing a root of the sample covariance matrix to reduce the spread of the associated eigenvalues. Through extensive numerical results, using both simulated and real datasets, we show that the proposed modification improves the glasso procedure. Our results reveal that the square-root improvement can be a reasonable choice in practice. Supplementary material for this article is available online.
PB Taylor & Francis
SN 1061-8600
YR 2017
FD 2017-10-13
LK https://hdl.handle.net/10016/32936
UL https://hdl.handle.net/10016/32936
LA eng
NO Andrés M. Alonso gratefully acknowledges financial support from CICYT Grants ECO2012-38442 and CO2015-66593.  Francisco J. Nogales and Vahe Avagyan were supported by the Spanish Government through project MTM2013-44902-P.
DS e-Archivo
RD 27 jul. 2024