Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 1 (2007), 597-615.
Smoothing ℓ1-penalized estimators for high-dimensional time-course data
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.
Electron. J. Statist., Volume 1 (2007), 597-615.
First available in Project Euclid: 10 December 2007
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
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