Smoothing ℓ₁-penalized estimators for high-dimensional time-course data
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Date
2007
Publication Type
Journal Article
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yes
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Abstract
When 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 ℓ1-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.
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Publication status
published
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Journal / series
Volume
1
Pages / Article No.
597 - 615
Publisher
Cornell University
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Edition / version
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Software
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Subject
Lasso; Local least squares; Multivariate regression; Variable selection; Weighted likelihood
Organisational unit
03502 - Bühlmann, Peter L. / Bühlmann, Peter L.