Smoothing $\ell_1$-penalized estimators for high-dimensional time-course data

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dc.creatorMeier, Lukas
dc.creatorBühlmann, Peter
dc.date2007-12-11
dc.date.accessioned2026-07-07T08:49:28Z
dc.date.available2026-07-07T08:49:28Z
dc.descriptionWhen a series of (related) linear models has to be estimated it is often appropriate to combine the different data-sets to construct more efficient estimators. We use $\ell_1$-penalized estimators like the Lasso or the Adaptive Lasso which can simultaneously do parameter estimation and model selection. We show that for a time-course of high-dimensional linear models the convergence rates of the Lasso and of the Adaptive Lasso can be improved by combining the different time-points in a suitable way. Moreover, the Adaptive Lasso still enjoys oracle properties and consistent variable selection. The finite sample properties of the proposed methods are illustrated on simulated data and on a real problem of motif finding in DNA sequences.
dc.descriptionPublished in at http://dx.doi.org/10.1214/07-EJS103 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0712.1654
dc.identifierhttp://arxiv.org/abs/0712.1654
dc.identifierElectronic Journal of Statistics 2007, Vol. 1, 597-615
dc.identifierdoi:10.1214/07-EJS103
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/144310
dc.subjectStatistics Theory
dc.subject62J07 (Primary); 62J99, 62H12 (Secondary)
dc.titleSmoothing $\ell_1$-penalized estimators for high-dimensional time-course data
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