Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Penalized Composite Quasi-Likelihood for Ultrahigh-Dimensional Variable
  Selection   In high-dimensional model selection problems, penalized simple least-square
approaches have been extensively used. This paper addresses the question of
both robustness and efficiency of penalized model selection methods, and
proposes a data-driven weighted linear combination of convex loss functions,
together with weighted $L_1$-penalty. It is completely data-adaptive and does
not require prior knowledge of the error distribution. The weighted
$L_1$-penalty is used both to ensure the convexity of the penalty term and to
ameliorate the bias caused by the $L_1$-penalty. In the setting with
dimensionality much larger than the sample size, we establish a strong oracle
property of the proposed method that possesses both the model selection
consistency and estimation efficiency for the true non-zero coefficients. As
specific examples, we introduce a robust method of composite L1-L2, and optimal
composite quantile method and evaluate their performance in both simulated and
real data examples.

23
The categories of this paper are: solv-int
1
The categories of this paper are: cs
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph