Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Smoothing $\ell_1$-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 $\ell_1$-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.

18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc