D-trace estimation of a precision matrix using adaptive Lasso penalties

Vahe Avagyan (), Andrés M. Alonso () and Francisco J. Nogales ()
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Vahe Avagyan: Ghent University
Andrés M. Alonso: Universidad Carlos III de Madrid
Francisco J. Nogales: Universidad Carlos III de Madrid

Advances in Data Analysis and Classification, 2018, vol. 12, issue 2, No 11, 425-447

Abstract: Abstract The accurate estimation of a precision matrix plays a crucial role in the current age of high-dimensional data explosion. To deal with this problem, one of the prominent and commonly used techniques is the $$\ell _1$$ ℓ 1 norm (Lasso) penalization for a given loss function. This approach guarantees the sparsity of the precision matrix estimate for properly selected penalty parameters. However, the $$\ell _1$$ ℓ 1 norm penalization often fails to control the bias of obtained estimator because of its overestimation behavior. In this paper, we introduce two adaptive extensions of the recently proposed $$\ell _1$$ ℓ 1 norm penalized D-trace loss minimization method. They aim at reducing the produced bias in the estimator. Extensive numerical results, using both simulated and real datasets, show the advantage of our proposed estimators.

Keywords: Adaptive thresholding; D-trace loss; Gaussian graphical model; Gene expression data; High-dimensionality; 62H30 Classification and discrimination; cluster analysis, 62J10 Analysis of variance and covariance, 65S05 Graphical methods (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-016-0272-8

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