Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the adaptive elastic-net with a diverging number of parameters   We consider the problem of model selection and estimation in situations where
the number of parameters diverges with the sample size. When the dimension is
high, an ideal method should have the oracle property [J. Amer. Statist. Assoc.
96 (2001) 1348--1360] and [Ann. Statist. 32 (2004) 928--961] which ensures the
optimal large sample performance. Furthermore, the high-dimensionality often
induces the collinearity problem, which should be properly handled by the ideal
method. Many existing variable selection methods fail to achieve both goals
simultaneously. In this paper, we propose the adaptive elastic-net that
combines the strengths of the quadratic regularization and the adaptively
weighted lasso shrinkage. Under weak regularity conditions, we establish the
oracle property of the adaptive elastic-net. We show by simulations that the
adaptive elastic-net deals with the collinearity problem better than the other
oracle-like methods, thus enjoying much improved finite sample performance.

37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
1
The categories of this paper are: cs
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga