Improving the graphical lasso estimation for the precision matrix through roots ot the sample convariance matrix

Vahe Avagyan and Francisco J. Nogales

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: In this paper, we focus on the estimation of a high-dimensional precision matrix. We propose a simple improvement of the graphical lasso framework (glasso) that is able to attain better statistical performance without sacrificing too much the computational cost. The proposed improvement is based on computing a root of the covariance matrix to reduce the spread of the associated eigenvalues, and maintains the original convergence rate. Through extensive numerical results, using both simulated and real datasets, we show the proposed modification outperforms the glasso procedure. Finally, our results show that the square-root improvement may be a reasonable choice in practice

Keywords: Gaussian; Graphical; Models; Gene; expression; High-dimensionality; Inverse; covariance; matrix; Penalized; estimation; Portfolio; selection; Root; of; a; matrix (search for similar items in EconPapers)
Date: 2014-05
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:ws141208

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