Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Penalized EM algorithm and copula skeptic graphical models for inferring
  networks for mixed variables   In this article, we consider the problem of reconstructing networks for
continuous, binary, count and discrete ordinal variables by estimating sparse
precision matrix in Gaussian copula graphical models. We propose two
approaches: $\ell_1$ penalized extended rank likelihood with Monte Carlo
Expectation-Maximization algorithm (copula EM glasso) and copula skeptic with
pair-wise copula estimation for copula Gaussian graphical models. The proposed
approaches help to infer networks arising from nonnormal and mixed variables.
We demonstrate the performance of our methods through simulation studies and
analysis of breast cancer genomic and clinical data and maize genetics data.

23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph