Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse Estimation of Generalized Linear Models (GLM) via Approximated
  Information Criteria   We propose a new sparse estimation method, termed MIC (Minimum approximated
Information Criterion), for generalized linear models (GLM) in fixed
dimensions. What is essentially involved in MIC is the approximation of the
$\ell_0$-norm with a continuous unit dent function. Besides, a
reparameterization step is devised to enforce sparsity in parameter estimates
while maintaining the smoothness of the objective function. MIC yields superior
performance in sparse estimation by optimizing the approximated information
criterion without reducing the search space and is computationally advantageous
since no selection of tuning parameters is required. Moreover, the
reparameterization tactic leads to valid significance testing results that are
free of post-selection inference. We explore the asymptotic properties of MIC
and illustrate its usage with both simulated experiments and empirical
examples.

25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys