Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evaluation and selection of models for out-of-sample prediction when the
  sample size is small relative to the complexity of the data-generating
  process   In regression with random design, we study the problem of selecting a model
that performs well for out-of-sample prediction. We do not assume that any of
the candidate models under consideration are correct. Our analysis is based on
explicit finite-sample results. Our main findings differ from those of other
analyses that are based on traditional large-sample limit approximations
because we consider a situation where the sample size is small relative to the
complexity of the data-generating process, in the sense that the number of
parameters in a `good' model is of the same order as sample size. Also, we
allow for the case where the number of candidate models is (much) larger than
sample size.

26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys