Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model Selection in High-Dimensional Misspecified Models   Model selection is indispensable to high-dimensional sparse modeling in
selecting the best set of covariates among a sequence of candidate models. Most
existing work assumes implicitly that the model is correctly specified or of
fixed dimensions. Yet model misspecification and high dimensionality are common
in real applications. In this paper, we investigate two classical
Kullback-Leibler divergence and Bayesian principles of model selection in the
setting of high-dimensional misspecified models. Asymptotic expansions of these
principles reveal that the effect of model misspecification is crucial and
should be taken into account, leading to the generalized AIC and generalized
BIC in high dimensions. With a natural choice of prior probabilities, we
suggest the generalized BIC with prior probability which involves a logarithmic
factor of the dimensionality in penalizing model complexity. We further
establish the consistency of the covariance contrast matrix estimator in a
general setting. Our results and new method are supported by numerical studies.

31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
1
The categories of this paper are: cs
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph