Electronic Journal of Statistics

Smoothing 1-penalized estimators for high-dimensional time-course data

Lukas Meier and Peter Bühlmann

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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 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.

Article information

Electron. J. Statist., Volume 1 (2007), 597-615.

First available in Project Euclid: 10 December 2007

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62J07: Ridge regression; shrinkage estimators
Secondary: 62J99: None of the above, but in this section 62H12: Estimation

Lasso Local least squares Multivariate regression Variable selection Weighted likelihood


Meier, Lukas; Bühlmann, Peter. Smoothing ℓ 1 -penalized estimators for high-dimensional time-course data. Electron. J. Statist. 1 (2007), 597--615. doi:10.1214/07-EJS103. https://projecteuclid.org/euclid.ejs/1197320663

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