D-trace Precision Matrix Estimation Using Adaptive Lasso Penalties

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dc.contributor.author Avagyan, Vahe
dc.contributor.author Alonso, Andrés M.
dc.contributor.author Nogales, Francisco J.
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.contributor.other
dc.date.accessioned 2015-10-19T11:27:33Z
dc.date.available 2015-10-19T11:27:33Z
dc.date.issued 2015-10-01
dc.identifier.issn 2387-0303
dc.identifier.uri http://hdl.handle.net/10016/21775
dc.description.abstract An accurate estimation of a precision matrix has 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 l1 norm (Lasso) penalization for a given loss function. This approach guarantees the sparsity of the precision matrix estimator for properly selected penalty parameters. However, the l1 norm penalization often fails to control the bias of the obtained estimator because of its overestimation behavior. In this paper, we introduce two adaptive extensions of the recently proposed l1 norm penalized D-trace loss minimization method. The proposed approaches intend to diminish the produced bias in the estimator. Extensive numerical results, using both simulated and real datasets, show the advantage of our proposed estimators.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 15-20
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Adaptive lasso
dc.subject.other D-trace loss
dc.subject.other Gaussian Graphical Model
dc.subject.other Gene expression data
dc.subject.other High-dimensionality
dc.title D-trace Precision Matrix Estimation Using Adaptive Lasso Penalties
dc.type workingPaper
dc.rights.accessRights openAccess
dc.type.version submitedVersion
dc.identifier.uxxi DT/0000001407
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