Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The restricted consistency property of leave-$n_v$-out cross-validation
  for high-dimensional variable selection   Cross-validation (CV) methods are popular for selecting the tuning parameter
in the high-dimensional variable selection problem. We show the mis-alignment
of the CV is one possible reason of its over-selection behavior. To fix this
issue, we propose a version of leave-$n_v$-out cross-validation (CV($n_v$)),
for selecting the optimal model among the restricted candidate model set for
high-dimensional generalized linear models. By using the same candidate model
sequence and a proper order of construction sample size $n_c$ in each CV split,
CV($n_v$) avoids the potential hurdles in developing theoretical properties.
CV($n_v$) is shown to enjoy the restricted model selection consistency property
under mild conditions. Extensive simulations and real data analysis support the
theoretical results and demonstrate the performances of CV($n_v$) in terms of
both model selection and prediction.

23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess